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Fix expert grad scaling problem with ZeRO optimizer #6546

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Oct 23, 2024
Merged

Fix expert grad scaling problem with ZeRO optimizer #6546

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607d8c9
Fix Expert Grad Scale Problem With Zero Optimizer
Sep 17, 2024
5a44f8c
remove useless code
Sep 18, 2024
b1231c4
remove useless comments
Sep 18, 2024
14d002d
Merge branch 'master' into fix_expert_weight_grad_with_zero
tohtana Sep 27, 2024
76dda2a
Merge branch 'master' into fix_expert_weight_grad_with_zero
loadams Sep 27, 2024
d0de160
Merge branch 'master' into fix_expert_weight_grad_with_zero
tohtana Oct 11, 2024
28b2aff
Merge branch 'master' into fix_expert_weight_grad_with_zero
tohtana Oct 14, 2024
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4 changes: 1 addition & 3 deletions deepspeed/runtime/zero/stage_1_and_2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1076,7 +1076,6 @@ def average_tensor(self, tensor):
if self.ipg_bucket_has_moe_params:
process_group = self.expert_dp_process_group[param.group_name] if is_moe_param(
param) else self.dp_process_group
grad_reduc.data.div_(dist.get_world_size(group=process_group) / float(self.sequence_parallel_size))

partition_ids = self.param_to_partition_ids[i][param_id]
assert all([p_id < dist.get_world_size(group=process_group) for p_id in partition_ids
Expand Down Expand Up @@ -1115,8 +1114,7 @@ def average_tensor(self, tensor):
curr_size += numel
prev_id, prev_process_group = partition_id, process_group

if not self.ipg_bucket_has_moe_params:
tensor.div_(dist.get_world_size(group=self.dp_process_group) / float(self.sequence_parallel_size))
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If only grad for expert is not correct, we only need to make 'grad_reduc' divide edp_world_size -> divide dp_world_size, why we need use 'tensor' for divide, it may contain more data not only gradient ? I just feel confused about here.

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In my understanding, there are only gradients waiting to do all-reduce in 'tensor'.

From the code, 'tensor' may be a buffer in 'self.ipg_buffer' or the gradient of 'self.extra_large_param_to_reduce' . So, 'tensor' is composed of data from one or more weight gradients, and the data pointer of 'grad_reduc' points to an address within 'tensor'.

According to the comments in the code, the logic of the old version code is:

  • Averages gradients at parameter level if ipg has a moe param, i.e. do average on 'grad_reduc'
  • Otherwise averaging is done at the entire buffer level at the end of the loop, i.e. do average on 'tensor'.

He did this because he wanted to divide the expert gradient by edp_size and the non-expert gradient by dp_size, so he must do the average at the parameter level when there is a moe param. But in our PR, we divide all weight gradients by dp_size, so we can directly do the average at the entire buffer level.

In addition, maybe I need also delete those old comments.

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Thank you for clarification, I agree with you for deleting those old comments.

tensor.div_(dist.get_world_size(group=self.dp_process_group) / float(self.sequence_parallel_size))

buckets = {}
for i, (dst, bucket_offset, numel) in enumerate(rank_and_offsets):
Expand Down
112 changes: 112 additions & 0 deletions tests/unit/moe/test_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
import deepspeed
import pytest
import gc
import random
from unit.common import DistributedTest
from unit.simple_model import SimplePRMoEModel, SimpleMoEModel, sequence_dataloader
import deepspeed.comm as dist
Expand Down Expand Up @@ -238,3 +239,114 @@ def check_equal(logits, cap, sparse_truth, res):
[2, 1, 1], [2, 2, 1], [2, 3, 1], [3, 0, 0]])
position_dispatch_res = topkgating(logits2, 3, 1, min_capacity=1, drop_policy='position')[2]
check_equal(logits2, 2, position_sec_sparse, position_dispatch_res)


class TestExpertWeightGradWithZero(DistributedTest):
world_size = 2

@pytest.mark.parametrize("zero_stage", [0, 1, 2])
def test(self, zero_stage):

if not required_torch_version(min_version=1.8):
pytest.skip("DeepSpeed MoE tests need torch 1.8 or higher to run correctly")

def seed_everything(seed=11):
random.seed(seed)
torch.manual_seed(seed)
get_accelerator().manual_seed(seed)
get_accelerator().manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

def get_state_dict_ep2(state_dict):
"""
convert state_dict from EP=1 to EP=2
"""
rank = int(deepspeed.comm.get_rank())
ep_state_dict = dict()
dst_sub_key = f"deepspeed_moe.experts.deepspeed_experts.0"
src_sub_key = f"deepspeed_moe.experts.deepspeed_experts.{rank}"
for moe_layer in ["moe_1", "moe_2"]:
for mlp_in_moe in [0, 1]:
dst_key = f"{moe_layer}.{dst_sub_key}.{mlp_in_moe}"
src_key = f"{moe_layer}.{src_sub_key}.{mlp_in_moe}"
ep_state_dict[f"{dst_key}.weight"] = state_dict[f"{src_key}.weight"].detach().clone()
ep_state_dict[f"{dst_key}.bias"] = state_dict[f"{src_key}.bias"].detach().clone()

for key in state_dict.keys():
if "deepspeed_moe.experts.deepspeed_experts" not in key:
ep_state_dict[key] = state_dict[key].detach().clone()
return ep_state_dict

def get_models(hidden_dim):
model_ep1 = SimpleMoEModel(hidden_dim=hidden_dim, num_experts=2, ep_size=1, use_rts=False)
model_ep2 = SimpleMoEModel(hidden_dim=hidden_dim, num_experts=2, ep_size=2, use_rts=False)

state_dict_ep1 = model_ep1.state_dict()
state_dict_ep2 = get_state_dict_ep2(state_dict_ep1)
model_ep2.load_state_dict(state_dict_ep2)

model_ep1, _, _, _ = deepspeed.initialize(config=config_dict, model=model_ep1)
model_ep2, _, _, _ = deepspeed.initialize(config=config_dict, model=model_ep2)

return model_ep1, model_ep2

def extract_expert_grad(model, expert_id):

def _get_weight_bias(experts):
return ([deepspeed.utils.safe_get_full_grad(expert[0].weight)
for expert in experts][expert_id].detach().clone(),
[deepspeed.utils.safe_get_full_grad(expert[0].bias)
for expert in experts][expert_id].detach().clone(),
[deepspeed.utils.safe_get_full_grad(expert[1].weight)
for expert in experts][expert_id].detach().clone(),
[deepspeed.utils.safe_get_full_grad(expert[1].bias)
for expert in experts][expert_id].detach().clone())

return (*_get_weight_bias(model.moe_1.deepspeed_moe.experts.deepspeed_experts),
*_get_weight_bias(model.moe_2.deepspeed_moe.experts.deepspeed_experts))

seed_everything()

config_dict = {
"train_micro_batch_size_per_gpu": 1,
"steps_per_print": 1,
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.1,
}
},
"zero_optimization": {
"stage": zero_stage
}
}

hidden_dim = 4
total_samples = 2
rank = deepspeed.comm.get_rank()
model_ep1, model_ep2 = get_models(hidden_dim)

data_loader = sequence_dataloader(model=model_ep1,
total_samples=total_samples,
hidden_dim=hidden_dim,
device=model_ep1.device,
dtype=torch.float32)
expert_weight_grad_ep1 = []
expert_weight_grad_ep2 = []
for batch in data_loader:
loss_ep1 = model_ep1(batch[0], batch[1])
loss_ep2 = model_ep2(batch[0], batch[1])

model_ep1.backward(loss_ep1)
model_ep2.backward(loss_ep2)

expert_weight_grad_ep1.extend(extract_expert_grad(model_ep1, rank))
expert_weight_grad_ep2.extend(extract_expert_grad(model_ep2, 0))

model_ep1.step()
model_ep2.step()

assert len(expert_weight_grad_ep1) == len(expert_weight_grad_ep2)
for grad_from_ep1, grad_from_ep2 in zip(expert_weight_grad_ep1, expert_weight_grad_ep2):
assert torch.allclose(grad_from_ep1, grad_from_ep2, atol=0, rtol=1e-4)
8 changes: 5 additions & 3 deletions tests/unit/simple_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ def forward(self, x, y, **kwargs):

class SimpleMoEModel(torch.nn.Module):

def __init__(self, hidden_dim, num_experts=4, ep_size=1, use_residual=False):
def __init__(self, hidden_dim, num_experts=4, ep_size=1, use_residual=False, use_rts=True):
super(SimpleMoEModel, self).__init__()
self.linear1 = torch.nn.Linear(hidden_dim, hidden_dim)
expert = torch.nn.Sequential(torch.nn.Linear(hidden_dim, hidden_dim), torch.nn.Linear(hidden_dim, hidden_dim))
Expand All @@ -89,7 +89,8 @@ def __init__(self, hidden_dim, num_experts=4, ep_size=1, use_residual=False):
ep_size=ep_size,
use_residual=use_residual,
num_experts=num_experts,
k=1)
k=1,
use_rts=use_rts)
# interleaving MoE modules with dense to create an opportunity
# for gradients to be merged in ZeRO stage 2 average_tensor reduce bucket
self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
Expand All @@ -98,7 +99,8 @@ def __init__(self, hidden_dim, num_experts=4, ep_size=1, use_residual=False):
ep_size=ep_size,
use_residual=use_residual,
num_experts=num_experts,
k=1)
k=1,
use_rts=use_rts)
self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim)
self.cross_entropy_loss = torch.nn.CrossEntropyLoss()

Expand Down
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