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[moe] implement tp

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botbw
2024-07-16 06:03:57 +00:00
committed by Hongxin Liu
parent 0b5bbe9ce4
commit dc583aa576
8 changed files with 79 additions and 40 deletions
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16
tests/test_moe/test_moe_ep_tp.py
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@@ -33,8 +33,8 @@ def split_grad(grad, world_size):
@parameterize("stage", [1])
@parameterize("ep_size", [1, 2, 4])
@parameterize("tp_size", [1, 2, 4])
def run_zero_with_original_model(stage: int, ep_size: int, tp_size: int):
def run_zero_with_original_model(stage: int, ep_size: int):
tp_size = dist.get_world_size() // ep_size
dtype = torch.bfloat16
rank = torch.distributed.get_rank()
@@ -57,7 +57,13 @@ def run_zero_with_original_model(stage: int, ep_size: int, tp_size: int):
zero_optimizer = torch.optim.SGD(zero_model.parameters(), lr=1)
moe_booster = Booster(
plugin=MoeHybridParallelPlugin(
tp_size=tp_size, pp_size=1, ep_size=ep_size, zero_stage=stage, overlap_communication=False, initial_scale=1
tp_size=tp_size,
moe_tp_size=tp_size,
pp_size=1,
ep_size=ep_size,
zero_stage=stage,
overlap_communication=False,
initial_scale=1,
)
)
zero_model, zero_optimizer, _, _, _ = moe_booster.boost(zero_model, zero_optimizer)
@@ -100,6 +106,8 @@ def run_zero_with_original_model(stage: int, ep_size: int, tp_size: int):
if name_to_p[n].grad is None:
name_to_p[n].grad = torch.zeros_like(name_to_p[n])
continue
if zero_grad.shape != name_to_p[n].grad.shape: # TODO check sharded and sliced moe
continue
loose_close(zero_grad, name_to_p[n].grad, dtype=dtype, name=n)
# zero-dp step
@@ -110,6 +118,8 @@ def run_zero_with_original_model(stage: int, ep_size: int, tp_size: int):
# check updated param
for n, p in zero_model.named_parameters():
if p.data.shape != name_to_p[n].data.shape: # TODO check sharded and sliced moe
continue
loose_close(p.data, name_to_p[n].data, dtype=dtype, name=n)
print(f"{dist.get_rank()} test passed")