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add moe topk(k>2) gate support #5881

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merged 8 commits into from
Aug 15, 2024
Merged

add moe topk(k>2) gate support #5881

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feb505c
Mingzhi/topk prob (#28)
inkcherry Aug 8, 2024
153af8e
refine
inkcherry Aug 8, 2024
80a0c5f
remove empty line
inkcherry Aug 8, 2024
6a2b767
Merge branch 'master' into topk_2
tjruwase Aug 9, 2024
3bea84b
fix format
inkcherry Aug 12, 2024
5582faf
Merge branch 'master' into topk_2
loadams Aug 14, 2024
e9409e2
Merge branch 'master' into topk_2
loadams Aug 14, 2024
cda9dd7
Merge branch 'master' into topk_2
tohtana Aug 15, 2024
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refine
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@inkcherry
inkcherry committed Aug 8, 2024
commit 153af8e2c7d84391a4b67754b67c2bedb19b5196
21 changes: 5 additions & 16 deletions deepspeed/moe/sharded_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -386,32 +386,27 @@ def topkgating(
# everything is in fp32 in this function
# get topk gates
top_gate, top_idx = torch.topk(logits, k=k, dim=1)
# gating decisions
gates = F.softmax(logits, dim=1)
num_experts = int(gates.shape[1])

# get topk mask
topk_masked_gates = torch.zeros_like(logits).scatter(1, top_idx, top_gate)

mask = torch.zeros_like(gates, dtype=torch.int64).scatter_(1, top_idx, 1)

# Compute tokens per expert
exp_counts = torch.sum(mask, dim=0).detach()
mask = torch.zeros_like(gates, dtype=torch.bool).scatter_(1, top_idx, 1)

# gating decisions
exp_counts = torch.sum(mask, dim=0).detach().to(logits.device)

# Compute l_aux
me = torch.mean(gates, dim=0)
# HPU Enable Begin
ce = torch.mean(mask.float(), dim=0, dtype=torch.float)
# HPU Enable End
ce = torch.mean(mask.float(), dim=0)
l_aux = torch.mean(me * ce) * num_experts * num_experts / k

if drop_tokens:
# Calculate configured capacity and remove locations outside capacity from mask
capacity = _capacity(gates, torch.tensor(capacity_factor * k), torch.tensor(min_capacity))
# update mask and locations by capacity
# mask *= torch.lt(locations, capacity)

if drop_policy == 'probs':
capacity_probs, capacity_indices = torch.topk(topk_masked_gates, k=capacity, dim=0, sorted=False)
capacity_mask = torch.zeros_like(logits).scatter(0, capacity_indices, 1)
Expand All @@ -421,7 +416,7 @@ def topkgating(
elif drop_policy == "position":
locations = torch.cumsum(mask, dim=0) - 1
mask *= torch.lt(locations, capacity)
else:
else:
raise ValueError(f"Invalid drop_policy: {drop_policy}")

else:
Expand All @@ -436,7 +431,6 @@ def topkgating(
new_capacity = torch.ceil(new_capacity / tp).mul(tp).to(new_capacity.dtype)
capacity = new_capacity


# normalize gates
gates_masked = gates * mask
gates_s = torch.sum(gates_masked, dim=-1, keepdim=True)
Expand Down Expand Up @@ -485,11 +479,6 @@ def __init__(self,
top2_2nd_expert_sampling: bool = True) -> None:
super().__init__()

# Only top-1 and top-2 are supported at the moment.
#if k != 1 and k != 2:
# raise ValueError('Only top-1 and top-2 gatings are supported.')
# HPU Enable Begin
# self.wg = torch.nn.Linear(model_dim, num_experts, bias=False).float()
self.wg = torch.nn.Linear(model_dim, num_experts, bias=False)
self.ep_group = ep_group
self.k = k
Expand Down
76 changes: 38 additions & 38 deletions tests/unit/moe/test_moe.py
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Expand Up @@ -13,7 +13,7 @@
from unit.simple_model import SimplePRMoEModel, SimpleMoEModel, sequence_dataloader
import deepspeed.comm as dist
from deepspeed import get_accelerator
from deepspeed.moe.sharded_moe import top1gating,topkgating
from deepspeed.moe.sharded_moe import top1gating, topkgating
from deepspeed.moe.utils import split_params_into_different_moe_groups_for_optimizer, is_moe_param
from deepspeed.utils.torch import required_torch_version

Expand Down Expand Up @@ -174,6 +174,7 @@ def test(self, ep_size, use_residual):
model.backward(loss)
model.step()


class TestTopk(DistributedTest):
world_size = 2

Expand All @@ -192,51 +193,50 @@ def test(self):
drop_tokens=False,
use_rts=True,
use_tutel=False)



class TestTopkGate(DistributedTest):

def test(self):
def check_equal(logits,cap,sparse_truth,res):
m,n=logits.shape
dispatch_mask_truth=torch.zeros(m,n,cap)
i,j,k=sparse_truth.t()
dispatch_mask_truth[i,j,k]=1
assert(torch.equal(dispatch_mask_truth, res))

def check_equal(logits, cap, sparse_truth, res):
m, n = logits.shape
dispatch_mask_truth = torch.zeros(m, n, cap)
i, j, k = sparse_truth.t()
dispatch_mask_truth[i, j, k] = 1
assert (torch.equal(dispatch_mask_truth, res))

#s=4 e=4 topk=2 cap=2(s*topk/e)
logits=torch.tensor([
[0.11,0.2,0.1,0.3],
[0.3,0.4,0.11,0.1],
[0.11,0.1,0.6,0.5],
[0.1,0.11,0.7,0.8]])
logits*=dist.get_rank()+1
probs_dispatch_res=topkgating(logits,2,1,min_capacity=1,drop_policy='probs')[2]
logits = torch.tensor([[0.11, 0.2, 0.1, 0.3], [0.3, 0.4, 0.11, 0.1], [0.11, 0.1, 0.6, 0.5],
[0.1, 0.11, 0.7, 0.8]])
logits *= dist.get_rank() + 1
probs_dispatch_res = topkgating(logits, 2, 1, min_capacity=1, drop_policy='probs')[2]
probs_sec_sparse = torch.tensor([[0, 1, 0], [1, 0, 0], [1, 1, 1], [2, 2, 0], [2, 3, 0], [3, 2, 1], [3, 3, 1]])
check_equal(logits,2,probs_sec_sparse,probs_dispatch_res)


position_sec_sparse =torch.tensor([[0,1,0],[0,3,0],[1,0,0],[1,1,1],[2,2,0],[2,3,1],[3,2,1]])
position_dispatch_res=topkgating(logits,2,1,min_capacity=1,drop_policy='position')[2]
check_equal(logits,2,position_sec_sparse,position_dispatch_res)

check_equal(logits, 2, probs_sec_sparse, probs_dispatch_res)

position_sec_sparse = torch.tensor([[0, 1, 0], [0, 3, 0], [1, 0, 0], [1, 1, 1], [2, 2, 0], [2, 3, 1],
[3, 2, 1]])
position_dispatch_res = topkgating(logits, 2, 1, min_capacity=1, drop_policy='position')[2]
check_equal(logits, 2, position_sec_sparse, position_dispatch_res)

#s=4 e=6 topk=3 cap=2(s*topk/e)
logits2=torch.tensor([[0.5858, 0.4801, 0.6269, 0.5397, 0.9722, 0.7034],
[0.5445, 0.6332, 0.4519, 0.6308, 0.0519, 0.6450],
[0.4874, 0.8110, 0.7467, 0.8474, 0.0277, 0.3068],
[0.8570, 0.6714, 0.5310, 0.3274, 0.4836, 0.9892]])
logits2*=dist.get_rank()+1
logits2 = torch.tensor([[0.5858, 0.4801, 0.6269, 0.5397, 0.9722, 0.7034],
[0.5445, 0.6332, 0.4519, 0.6308, 0.0519, 0.6450],
[0.4874, 0.8110, 0.7467, 0.8474, 0.0277, 0.3068],
[0.8570, 0.6714, 0.5310, 0.3274, 0.4836, 0.9892]])
logits2 *= dist.get_rank() + 1

#top3 full mask #prob_mask #postion_mask
#0 0 1 0 1 1 #0 0 1 0 1 1 #0 0 1 0 1 1
#0 0 1 0 1 1 #0 0 1 0 1 1 #0 0 1 0 1 1
#0 1 0 1 0 1 #0 0 0 1 0 0 #0 1 0 1 0 1
#0 1 1 1 0 0 #0 1 1 1 0 0 #0 1 1 1 0 0
#1 1 0 0 0 1 #1 1 0 0 0 1 #1 0 0 0 0 0
probs_dispatch_res=topkgating(logits2,3,1,min_capacity=1,drop_policy='probs')[2]
probs_sec_sparse = torch.tensor([[0,2,0],[0,4,0],[0,5,0],[1,3,0],[2,1,0],[2,2,1],[2,3,1],[3,0,0],[3,1,1],[3,5,1]])
check_equal(logits2,2, probs_sec_sparse,probs_dispatch_res)

position_sec_sparse =torch.tensor([[0,2,0],[0,4,0],[0,5,0],[1,1,0],[1,3,0],[1,5,1],[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)



probs_dispatch_res = topkgating(logits2, 3, 1, min_capacity=1, drop_policy='probs')[2]
probs_sec_sparse = torch.tensor([[0, 2, 0], [0, 4, 0], [0, 5, 0], [1, 3, 0], [2, 1, 0], [2, 2, 1], [2, 3, 1],
[3, 0, 0], [3, 1, 1], [3, 5, 1]])
check_equal(logits2, 2, probs_sec_sparse, probs_dispatch_res)

position_sec_sparse = torch.tensor([[0, 2, 0], [0, 4, 0], [0, 5, 0], [1, 1, 0], [1, 3, 0], [1, 5, 1],
[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)