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[v0.11.0] [Bugfix] [MoE]fix error in deepseek when using allgather (#3827)

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### What this PR does / why we need it?
After refactoring vllm_ascend/models and FusedMoE, we are unable to pass
`gate` from deepseekv2.py to `AscendFusedMoE.forward`, which will result
in error when running deepseek v3/r1 with allgather.
Hence, this pr removes `gate` related computations from FusedMoE module
in eager/aclgraph mode.
### Does this PR introduce _any_ user-facing change?
`rm_router_logits` is deprecated in eager/aclgraph.
### How was this patch tested?
e2e & ut

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
This commit is contained in:
weichen
2025-10-30 14:59:46 +08:00
committed by GitHub
parent 211d4b9da4
commit c506ba60fb
7 changed files with 98 additions and 115 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
tests/ut/ops/test_fused_moe_prepare_and_finalize.py
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@@ -191,13 +191,7 @@ class TestFusedMoEPrepareAndFinalize(unittest.TestCase):
hidden_states = torch.randn(3, 8)
router_logits = torch.randn(3, 2)
# Mock the gate function for rm_router_logits=False case
mock_gate = MagicMock()
mock_gate.return_value = (router_logits.repeat(2, 1), None)
h_out, r_out, _ = layer.prepare(hidden_states,
router_logits,
gate=mock_gate)
h_out, r_out, _ = layer.prepare(hidden_states, router_logits)
# After all-gather with DP=2, should double the batch size
self.assertEqual(h_out.shape[0], 12)
@@ -258,14 +252,8 @@ class TestFusedMoEPrepareAndFinalize(unittest.TestCase):
hidden_states = torch.randn(3, 8)
router_logits = torch.randn(3, 2)
# Mock gate for router logits recomputation
mock_gate = MagicMock()
mock_gate.return_value = (torch.randn(7, 2), None)
# Run prepare
h_out, r_out, _ = layer.prepare(hidden_states,
router_logits,
gate=mock_gate)
h_out, r_out, _ = layer.prepare(hidden_states, router_logits)
# Should be global tensor: [7, 8] and [7, 2]
self.assertEqual(h_out.shape, (7, 8))

6
tests/ut/ops/test_moe_comm_method.py
View File

@@ -63,7 +63,7 @@ class TestMoECommMethod(TestBase):
# Verify prepare was called with correct arguments
mock_pf_instance.prepare.assert_called_once_with(
hidden_states, router_logits, False, False, None)
hidden_states, router_logits, False, False)
# Test finalize method
comm_impl.finalize(h_out, reduce_results=True)
@@ -108,7 +108,7 @@ class TestMoECommMethod(TestBase):
# Verify prepare was called with correct arguments
mock_pf_instance.prepare.assert_called_once_with(
hidden_states, router_logits, False, False, None)
hidden_states, router_logits, False, False)
# Test finalize method
comm_impl.finalize(h_out, reduce_results=True)
@@ -153,7 +153,7 @@ class TestMoECommMethod(TestBase):
# Verify prepare was called with correct arguments
mock_pf_instance.prepare.assert_called_once_with(
hidden_states, router_logits, False, False, None)
hidden_states, router_logits, False, False)
@patch("vllm_ascend.ops.moe.moe_comm_method.get_current_vllm_config")
@patch("vllm_ascend.ops.moe.moe_comm_method.get_forward_context")

106
vllm_ascend/ops/moe/fused_moe_prepare_and_finalize.py
View File

@@ -26,7 +26,7 @@ from vllm.distributed.parallel_state import (
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe import FusedMoEConfig
from vllm_ascend.utils import enable_sp, get_rm_router_logits_state
from vllm_ascend.utils import enable_sp
class FusedMoEPrepareAndFinalize(ABC):
@@ -43,31 +43,26 @@ class FusedMoEPrepareAndFinalize(ABC):
def __init__(self, moe_config: FusedMoEConfig):
self.moe_config = moe_config
is_deepseek_v3_r1 = self.moe_config.original_num_experts == 256
self.rm_router_logits = get_rm_router_logits_state(
self.moe_config.ep_size, self.moe_config.dp_size,
is_deepseek_v3_r1)
@abstractmethod
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def prepare(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Prepare tensors before MoE computation. May involve:
- Padding to align communication boundaries
- Slicing across tensor-parallel ranks
- Broadcasting across data-parallel ranks
- Recomputing router logits if needed
Args:
hidden_states (torch.Tensor): Input features, shape [num_tokens, hidden_size]
router_logits (torch.Tensor): Router outputs, shape [num_tokens, num_experts]
enable_shared_expert_dp (bool): Skip DP communication for shared experts
replace_allreduce (bool): Bypass default all-reduce behavior
gate (nn.Module, optional): Gate network to recompute router_logits if needed
Returns:
Tuple of:
@@ -116,12 +111,13 @@ class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def prepare(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Preparation steps:
1. Fetch `mc2_mask` and target padding length from forward context.
@@ -214,12 +210,13 @@ class FusedMoEPrepareAndFinalizeWithAll2All(FusedMoEPrepareAndFinalize):
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def prepare(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Preparation steps:
1. Pad hidden_states and router_logits to next multiple of TP size.
@@ -307,12 +304,13 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
TP AG → Attn → TP RS → EP AG → MoE → EP RS
"""
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def prepare(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Preparation steps:
AllGather hidden_states and router_logits to form global tensors.
@@ -325,7 +323,7 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
return self._prepare_with_dp_group(hidden_states, router_logits,
enable_shared_expert_dp,
replace_allreduce, gate)
replace_allreduce)
def _prepare_with_ep_group(
self,
@@ -340,12 +338,12 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
return hidden_states, router_logits, None
def _prepare_with_dp_group(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Preparation steps:
1. Fetch max token count across DP group from forward context.
@@ -365,18 +363,14 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
if not self.rm_router_logits:
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
# All-gather across DP group
hidden_states = self.moe_config.dp_group.all_gather(
hidden_states, 0)
if self.rm_router_logits:
router_logits, _ = gate(hidden_states) # Recompute globally
else:
router_logits = self.moe_config.dp_group.all_gather(
router_logits, 0)
router_logits = self.moe_config.dp_group.all_gather(
router_logits, 0)
return hidden_states, router_logits, None
@@ -472,12 +466,13 @@ class FusedMoEPrepareAndFinalizeWithNaiveMulticast(FusedMoEPrepareAndFinalize):
get_dp_group().broadcast(buffer[start:end, :], idx)
return buffer
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def prepare(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Preparation steps:
1. Fetch cumulative token boundaries from forward context.
@@ -493,11 +488,8 @@ class FusedMoEPrepareAndFinalizeWithNaiveMulticast(FusedMoEPrepareAndFinalize):
).dp_metadata.cu_tokens_across_sp(1)
hidden_states = self._naive_multicast(hidden_states,
self.cu_tokens_across_dp_cpu)
if self.rm_router_logits:
router_logits, _ = gate(hidden_states)
else:
router_logits = self._naive_multicast(
router_logits, self.cu_tokens_across_dp_cpu)
router_logits = self._naive_multicast(router_logits,
self.cu_tokens_across_dp_cpu)
return hidden_states, router_logits, None

15
vllm_ascend/ops/moe/moe_comm_method.py
View File

@@ -63,15 +63,16 @@ class MoECommMethod(ABC):
self.fused_moe_prepare_finalize = self._get_fused_moe_prepare_finalize(
)
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor]:
def prepare(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
replace_allreduce: bool = False
) -> tuple[torch.Tensor, torch.Tensor]:
hidden_states, router_logits, mc2_mask = self.fused_moe_prepare_finalize.prepare(
hidden_states, router_logits, enable_shared_expert_dp,
replace_allreduce, gate)
replace_allreduce)
self.mc2_mask = mc2_mask
return hidden_states, router_logits

8
vllm_ascend/torchair/ops/torchair_fused_moe.py
View File

@@ -48,12 +48,12 @@ from vllm_ascend.eplb.core.eplb_utils import (determine_default_expert_map,
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.quantization.quant_config import AscendFusedMoEMethod
from vllm_ascend.torchair.ops.sequence_parallel import MetadataForPadding
from vllm_ascend.torchair.utils import (npu_stream_switch, npu_wait_tensor,
from vllm_ascend.torchair.utils import (get_all_reduce_merge_state,
get_rm_router_logits_state,
npu_stream_switch, npu_wait_tensor,
super_kernel)
from vllm_ascend.utils import (AscendSocVersion, dispose_tensor,
get_all_reduce_merge_state,
get_ascend_soc_version,
get_rm_router_logits_state, is_310p,
get_ascend_soc_version, is_310p,
is_hierarchical_communication_enabled)

32
vllm_ascend/torchair/utils.py
View File

@@ -15,6 +15,8 @@ try:
except ImportError:
from torchair.ops import NpuStreamSwitch as _npu_stream_switch
from torchair.ops import npu_wait_tensor as _npu_wait_tensor
import vllm_ascend.envs as envs_ascend
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_enable_nz
KV_CACHE_BYTES_CACHE_PATH_NAME = ".kv_cache_bytes"
@@ -241,3 +243,33 @@ def torchair_ops_patch():
def super_kernel(prefix: str, option: str, enabled: bool = True):
return _super_kernel(prefix, option) if enabled else nullcontext()
# TODO(ttanzhiqiang): rm_router_logits
# dp>1 will trigger
# In theory, this solution is only applicable to AllGather and AllGatherEP, because in the dp scenario, the previous operation was gate + two communications, and now it is changed to one communication + gate operation, which can save some communication time. In theory, all moe AllGather and AllGatherEP solutions can follow this logic, but now other moe models (qwen3-235b) dp solutions are not adjusted, so use the switch to control it to prevent code errors.
def get_rm_router_logits_state(ep_size: int, dp_size: int,
is_deepseek_v3_r1: bool):
# the fusion operator torch_npu.npu_grouped_matmul_finalize_routing called by allgather ep
# only supports deepseek v3/r1
if dp_size > 1:
if (envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and ep_size > 1
and is_deepseek_v3_r1):
return True
elif ep_size == 1 and is_deepseek_v3_r1:
return True
return False
# TODO(ttanzhiqiang): all_reduce merge
# When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
# Currently, all_reduce_merge is enabled by default in the AllGather, AllGatherEP and NaiveMulticast scenarios of the deepseek model.
def get_all_reduce_merge_state(ep_size: int, is_deepseek_v3_r1: bool):
# the fusion operator torch_npu.npu_grouped_matmul_finalize_routing called by allgather ep
# only supports deepseek v3/r1
if (envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and ep_size > 1
and is_deepseek_v3_r1):
return True
elif ep_size == 1 and is_deepseek_v3_r1:
return True
return False

30
vllm_ascend/utils.py
View File

@@ -520,36 +520,6 @@ class ProfileExecuteDuration:
return durations
# TODO(ttanzhiqiang): rm_router_logits
# dp>1 will trigger
# In theory, this solution is only applicable to AllGather and AllGatherEP, because in the dp scenario, the previous operation was gate + two communications, and now it is changed to one communication + gate operation, which can save some communication time. In theory, all moe AllGather and AllGatherEP solutions can follow this logic, but now other moe models (qwen3-235b) dp solutions are not adjusted, so use the switch to control it to prevent code errors.
def get_rm_router_logits_state(ep_size: int, dp_size: int,
is_deepseek_v3_r1: bool):
# the fusion operator torch_npu.npu_grouped_matmul_finalize_routing called by allgather ep
# only supports deepseek v3/r1
if dp_size > 1:
if (envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and ep_size > 1
and is_deepseek_v3_r1):
return True
elif ep_size == 1 and is_deepseek_v3_r1:
return True
return False
# TODO(ttanzhiqiang): all_reduce merge
# When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
# Currently, all_reduce_merge is enabled by default in the AllGather, AllGatherEP and NaiveMulticast scenarios of the deepseek model.
def get_all_reduce_merge_state(ep_size: int, is_deepseek_v3_r1: bool):
# the fusion operator torch_npu.npu_grouped_matmul_finalize_routing called by allgather ep
# only supports deepseek v3/r1
if (envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and ep_size > 1
and is_deepseek_v3_r1):
return True
elif ep_size == 1 and is_deepseek_v3_r1:
return True
return False
def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
"""Register Ascend CustomOP