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[Bugfix] bugfix for moe_mlp in vllm-ascend/v0.11.0-dev (#4885)

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### What this PR does / why we need it?
This PR fixes a bug in the moe_mlp module by correcting the arguments
passed to the torch_npu.npu_dequant_swiglu_quant function.It properly
converts group_list from a cumulative sum to counts for the group_index
parameter.

### Does this PR introduce _any_ user-facing change?
No


- vLLM version: v0.12.0
- vLLM main: https://github.com/vllm-project/vllm/main

---------

Signed-off-by: tanqingshan (A)  <50050625@china.huawei.com>
Signed-off-by: tanqingshan (A) <50050625@china.huawei.com>
Co-authored-by: tanqingshan (A) <50050625@china.huawei.com>
Co-authored-by: Mercykid-bash <ruanche0218@gmail.com>
This commit is contained in:
Clorist33
2025-12-12 14:51:47 +08:00
committed by GitHub
parent 9c0ad46c1a
commit 4f0dddc9ee
5 changed files with 41 additions and 34 deletions
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4
tests/ut/eplb/core/test_eplb_device_transfer_loader.py
View File

@@ -47,8 +47,8 @@ def test_generate_task_and_state_flow(mock_adaptor):
loader_obj.state = loader.ExpertWeightUpdateState.WAITING
loader_obj.generate_expert_d2d_transfer_task([], [], {}, 0)
assert loader_obj.comm_op_list is None
assert loader_obj.state == loader.ExpertWeightUpdateState.WAITING
assert not loader_obj.comm_op_list
assert loader_obj.state == loader.ExpertWeightUpdateState.READY
def test_asyn_transfer_and_update(mock_adaptor):

12
tests/ut/ops/test_expert_load_balancer.py
View File

@@ -82,7 +82,7 @@ class TestExpertLoadBalancer(TestBase):
)
self.assertEqual(expert_placement_map.shape,
(self.expert_load_balancer.layers_num,
self.expert_load_balancer.ranks_num, 10))
self.expert_load_balancer.ranks_num, 8))
self.assertTrue(torch.all(expert_placement_map >= -1))
def test_generate_log2phy_expert_map(self):
@@ -90,7 +90,7 @@ class TestExpertLoadBalancer(TestBase):
log2phy_map = self.expert_load_balancer.generate_log2phy_expert_map(
layer_id)
self.assertEqual(log2phy_map.shape,
(self.expert_load_balancer.ranks_num, 10))
(self.expert_load_balancer.ranks_num, 8))
self.assertTrue(torch.all(log2phy_map >= -1))
@mock.patch("torch_npu.npu._lazy_init")
@@ -101,7 +101,7 @@ class TestExpertLoadBalancer(TestBase):
rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
layer_id, rank_id)
self.assertEqual(rank_local_expert_num, 5)
expected_tensor = torch.tensor([2, -1, 1, 3, -1, 4, -1, 0, -1, -1],
expected_tensor = torch.tensor([2, -1, 1, 3, -1, 4, -1, 0],
dtype=torch.int32).to(
rank_expert_map.device)
self.assertTrue(rank_expert_map.equal(expected_tensor))
@@ -109,7 +109,7 @@ class TestExpertLoadBalancer(TestBase):
rank_id = 1
rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
layer_id, rank_id)
expected_tensor = torch.tensor([-1, 1, 4, -1, 2, -1, 0, 3, -1, -1],
expected_tensor = torch.tensor([-1, 1, 4, -1, 2, -1, 0, 3],
dtype=torch.int32).to(
rank_expert_map.device)
self.assertTrue(rank_expert_map.equal(expected_tensor))
@@ -119,7 +119,7 @@ class TestExpertLoadBalancer(TestBase):
rank_id = 0
log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
layer_id, rank_id)
expected_tensor = torch.tensor([2, 6, 1, 3, 7, 4, 5, 0, -1, -1],
expected_tensor = torch.tensor([2, 6, 1, 3, 7, 4, 5, 0],
dtype=torch.int32).to(
log2phy_map.device)
self.assertTrue(log2phy_map.equal(expected_tensor))
@@ -127,7 +127,7 @@ class TestExpertLoadBalancer(TestBase):
rank_id = 1
log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
layer_id, rank_id)
expected_tensor = torch.tensor([2, 6, 9, 3, 7, 4, 5, 8, -1, -1],
expected_tensor = torch.tensor([2, 6, 9, 3, 7, 4, 5, 8],
dtype=torch.int32).to(
log2phy_map.device)
self.assertTrue(log2phy_map.equal(expected_tensor))

5
tests/ut/ops/test_fused_ops.py
View File

@@ -293,13 +293,13 @@ class TestCumsumGroupList(TestBase):
def test_cumsum_group_list_with_type_0(self):
group_list = self.experts.cumsum(dim=0)
group_list_type = 0
result = cumsum_group_list(group_list, group_list_type)
result = cumsum_group_list(group_list, group_list_type, 0)
self.assertTrue(torch.equal(result, self.group_list))
def test_cumsum_group_list_with_type_1(self):
group_list = self.experts
group_list_type = 1
result = cumsum_group_list(group_list, group_list_type)
result = cumsum_group_list(group_list, group_list_type, 0)
self.assertTrue(torch.equal(result, self.group_list))
def test_cumsum_group_list_with_type_2(self):
@@ -312,6 +312,7 @@ class TestCumsumGroupList(TestBase):
group_list_type = 2
result = cumsum_group_list(group_list,
group_list_type,
0,
active_num=self.active_num,
expert_num=self.expert_num)
self.assertTrue(torch.equal(result, self.group_list))

3
tests/ut/ops/test_token_dispatcher.py
View File

@@ -130,7 +130,7 @@ class TestTokenDispatcherWithMC2(TestBase):
self.dispatcher.need_extra_args = True
self.dispatcher.enable_dispatch_v2 = True
self.dispatcher.output = torch.randint(0, 8, (10, 1))
self.dispatcher.moe_expert_num = len(self.dispatcher.expert_map)
kwargs = self.dispatcher.get_combine_mc_kwargs(hidden_states)
self.assertIn("tp_send_counts", kwargs)
@@ -148,6 +148,7 @@ class TestTokenDispatcherWithMC2(TestBase):
self.dispatcher.enable_dispatch_v2 = True
self.dispatcher.swiglu_out_scale = torch.randint(0, 8, (10, 1))
self.dispatcher.output = torch.randint(0, 8, (10, 1))
self.dispatcher.moe_expert_num = len(self.dispatcher.expert_map)
self.hidden_states = torch.randn(10, 128)
with patch("torch_npu.npu_moe_distribute_combine_v2",

49
vllm_ascend/ops/moe/moe_mlp.py
View File

@@ -26,31 +26,39 @@ from vllm_ascend.utils import dispose_tensor, is_310p
def cumsum_group_list(group_list: torch.Tensor,
group_list_type: int,
src_list_type: int,
dst_list_type: int,
active_num: int = 0,
expert_num: int = 0) -> torch.Tensor:
if group_list_type not in [0, 1, 2]:
if src_list_type not in [0, 1, 2]:
raise ValueError(
f"group_list_type should be in [0, 1, 2], but received {group_list_type}"
f"group_list_type should be in [0, 1, 2], but received {src_list_type}"
)
if group_list_type == 0:
if src_list_type == dst_list_type:
return group_list
if group_list_type == 1:
if src_list_type == 1 and dst_list_type == 0:
return group_list.cumsum(dim=0)
if src_list_type == 0 and dst_list_type == 1:
group_diff = torch.diff(group_list)
new_group = torch.cat([group_diff[0].unsqueeze(0), group_diff], dim=0)
return new_group
if src_list_type == 2 and dst_list_type == 0:
experts = pad(group_list[:, 0], (1, 0))
tokens = pad(group_list[:, 1].cumsum(dim=0), (1, 0))
cumsum_group_list = torch.full(size=(expert_num, ),
fill_value=active_num,
dtype=group_list.dtype,
device=group_list.device)
experts = pad(group_list[:, 0], (1, 0))
tokens = pad(group_list[:, 1].cumsum(dim=0), (1, 0))
cumsum_group_list = torch.full(size=(expert_num, ),
fill_value=active_num,
dtype=group_list.dtype,
device=group_list.device)
for i, (start, end) in enumerate(zip(experts[:-1], experts[1:])):
if end > start:
cumsum_group_list[start:end] = tokens[i]
for i, (start, end) in enumerate(zip(experts[:-1], experts[1:])):
if end > start:
cumsum_group_list[start:end] = tokens[i]
return cumsum_group_list
return cumsum_group_list
raise NotImplementedError(
f"Conversion from src_list_type={src_list_type} to dst_list_type={dst_list_type} is not implemented yet. "
"This feature is under development.")
def quant_apply_mlp(hidden_states: torch.Tensor,
@@ -89,7 +97,7 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
x=hidden_states,
weight=w1,
group_list=cumsum_group_list(group_list, group_list_type),
group_list=cumsum_group_list(group_list, group_list_type, 0),
weight_scale=w1_scale,
x_scale=pertoken_scale)
else:
@@ -105,9 +113,6 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
group_list=group_list,
output_dtype=torch.int32)[0]
# act_fn: swiglu
group_diff = torch.diff(group_list)
new_group = torch.cat([group_list[0].unsqueeze(0), group_diff],
dim=0)
hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
x=hidden_states,
weight_scale=w1_scale,
@@ -115,7 +120,7 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=new_group,
group_index=cumsum_group_list(group_list, group_list_type, 1),
activate_left=True,
quant_mode=1,
)
@@ -148,7 +153,7 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
x=hidden_states,
weight=w1,
bias=bias1,
group_list=cumsum_group_list(group_list, group_list_type),
group_list=cumsum_group_list(group_list, group_list_type, 0),
weight_scale=w1_scale,
x_scale=pertoken_scale)
else: