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[Refactor] Delete dead code in models (#51838)

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Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Wentao Ye
2026-08-11 15:44:26 -04:00
committed by GitHub
parent ded6c452c3
commit fd04edef3e
25 changed files with 34 additions and 223 deletions
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vllm/lora/layers/fused_moe.py
-4
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@@ -466,10 +466,6 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
def runner(self) -> MoERunner:
return self.base_layer
@property
def is_internal_router(self) -> bool:
return self.base_layer.is_internal_router
@classmethod
def can_replace_layer(
cls,
vllm/model_executor/layers/fused_moe/runner/moe_runner.py
-4
View File
@@ -311,10 +311,6 @@ class MoERunner(MoERunnerInterface):
def shared_experts(self) -> SharedExperts | None:
return self._shared_experts
@property
def is_internal_router(self) -> bool:
return self.gate is not None
# TODO(bnell): Temporary hack. Get rid of this.
def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
self.routed_experts._replace_quant_method(quant_method)
vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
-5
View File
@@ -45,11 +45,6 @@ class MoERunnerInterface(PluggableLayer, ABC):
def shared_experts(self) -> SharedExperts | None:
raise NotImplementedError
@property
@abstractmethod
def is_internal_router(self) -> bool:
raise NotImplementedError
@property
@abstractmethod
def _quant_method(self) -> FusedMoEMethodBase:
vllm/model_executor/models/AXK1.py
+3 -15
View File
@@ -108,7 +108,6 @@ class AXK1MoE(nn.Module):
self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
assert config.n_routed_experts is not None
assert config.num_experts_per_tok is not None
@@ -148,11 +147,6 @@ class AXK1MoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
self.is_fusion_moe_shared_experts_enabled = (
rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
@@ -210,15 +204,9 @@ class AXK1MoE(nn.Module):
if self.is_sequence_parallel:
hidden_states = sequence_parallel_chunk(hidden_states)
if self.experts.is_internal_router:
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
else:
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
if self.is_sequence_parallel:
final_hidden_states = tensor_model_parallel_all_gather(
vllm/model_executor/models/afmoe.py
-6
View File
@@ -71,7 +71,6 @@ class AfmoeMoE(nn.Module):
self.route_norm = config.route_norm
self.ep_group = get_ep_group().device_group
self.ep_rank = self.ep_group.rank()
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.num_experts
self.n_shared_experts: int = config.num_shared_experts
@@ -103,11 +102,6 @@ class AfmoeMoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.shared_experts = None
# Shared experts
if config.num_shared_experts > 0:
vllm/model_executor/models/deepseek_v2.py
+3 -15
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@@ -293,7 +293,6 @@ class DeepseekV2MoE(nn.Module):
self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.n_routed_experts
self.n_shared_experts: int = config.n_shared_experts
@@ -329,11 +328,6 @@ class DeepseekV2MoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
self.is_fusion_moe_shared_experts_enabled = (
rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
@@ -409,15 +403,9 @@ class DeepseekV2MoE(nn.Module):
if self.is_sequence_parallel and not already_sequence_parallel:
hidden_states = sequence_parallel_chunk(hidden_states)
if self.experts.is_internal_router:
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
else:
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
if self.is_sequence_parallel and not already_sequence_parallel:
final_hidden_states = tensor_model_parallel_all_gather(
vllm/model_executor/models/ernie45_moe.py
-5
View File
@@ -129,7 +129,6 @@ class Ernie4_5_MoeMoE(nn.Module):
self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", None)
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.moe_num_experts
self.n_shared_experts: int = self.moe_num_shared_experts
@@ -143,10 +142,6 @@ class Ernie4_5_MoeMoE(nn.Module):
self.n_logical_experts = self.n_routed_experts
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.has_shared_experts = getattr(config, "moe_num_shared_experts", 0) > 0
if self.tp_size > config.moe_num_experts:
vllm/model_executor/models/exaone_moe.py
-6
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@@ -76,7 +76,6 @@ class ExaoneMoe(nn.Module):
self.routed_scaling_factor = config.routed_scaling_factor
self.ep_group = get_ep_group().device_group
self.ep_rank = self.ep_group.rank()
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -113,11 +112,6 @@ class ExaoneMoe(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
if getattr(config, "num_shared_experts", 0) > 0:
intermediate_size = config.moe_intermediate_size * config.num_shared_experts
self.shared_experts = ExaoneMoeGatedMLP(
vllm/model_executor/models/glm4_moe.py
-6
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@@ -127,7 +127,6 @@ class Glm4MoE(nn.Module):
self.routed_scaling_factor = config.routed_scaling_factor
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.n_routed_experts
self.n_shared_experts: int = config.n_shared_experts
@@ -160,11 +159,6 @@ class Glm4MoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
# AITER fused shared-expert (FSE) gate; mirrors the deepseek_v2.py
# pattern (see Glm4MoE / MoERunner wiring there).
self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
vllm/model_executor/models/hunyuan_v1.py
-6
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@@ -372,7 +372,6 @@ class HunYuanSparseMoeBlock(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -408,11 +407,6 @@ class HunYuanSparseMoeBlock(nn.Module):
self.n_redundant_experts = eplb_config.num_redundant_experts
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.gate = ReplicatedLinear(
config.hidden_size,
config.num_experts,
vllm/model_executor/models/hy_v3.py
-5
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@@ -134,7 +134,6 @@ class HYV3MoEFused(nn.Module):
super().__init__()
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
if self.tp_size > config.num_experts:
@@ -153,10 +152,6 @@ class HYV3MoEFused(nn.Module):
self.n_redundant_experts = eplb_config.num_redundant_experts
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.gate = GateLinear(
config.hidden_size,
config.num_experts,
vllm/model_executor/models/interns1_pro.py
-6
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@@ -144,7 +144,6 @@ class InternS1ProMoeSparseMoeBlock(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -165,11 +164,6 @@ class InternS1ProMoeSparseMoeBlock(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
# For custom routing function
self.n_groups = getattr(config, "router_n_groups", -1)
vllm/model_executor/models/interns2_mobius.py
+4 -11
View File
@@ -127,17 +127,10 @@ class InternS2MobiusMetaMoeBlock(nn.Module):
if self.is_sequence_parallel and not already_sequence_parallel:
hidden_states = sequence_parallel_chunk(hidden_states)
if self.experts.is_internal_router:
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
)
else:
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=router_logits,
)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
)
if self.is_sequence_parallel and not already_sequence_parallel:
final_hidden_states = tensor_model_parallel_all_gather(
vllm/model_executor/models/laguna.py
-6
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@@ -132,7 +132,6 @@ class LagunaMoE(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = self.ep_group.rank()
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -160,11 +159,6 @@ class LagunaMoE(nn.Module):
self.n_logical_experts = self.n_routed_experts
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
# Router gate
self.gate = ReplicatedLinear(
config.hidden_size,
vllm/model_executor/models/lfm2_moe.py
-6
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@@ -106,7 +106,6 @@ class Lfm2MoeSparseMoeBlock(nn.Module):
self.routed_scaling_factor = config.routed_scaling_factor
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -126,11 +125,6 @@ class Lfm2MoeSparseMoeBlock(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.gate = ReplicatedLinear(
config.hidden_size,
config.num_experts,
vllm/model_executor/models/mimo_v2.py
-6
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@@ -127,7 +127,6 @@ class MiMoV2MoE(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.n_routed_experts
@@ -154,11 +153,6 @@ class MiMoV2MoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
dtype = getattr(config, "moe_router_dtype", "float32")
self.gate_dtype = str_dtype_to_torch_dtype(dtype)
self.gate = nn.Linear(
vllm/model_executor/models/mixtral.py
-6
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@@ -92,7 +92,6 @@ class MixtralMoE(nn.Module):
self.hidden_size = hidden_size
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
# Expert Parallelism Load balancing settings.
@@ -105,11 +104,6 @@ class MixtralMoE(nn.Module):
self.n_redundant_experts = parallel_config.eplb_config.num_redundant_experts
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
# Gate always runs at half / full precision for now.
self.gate = ReplicatedLinear(
vllm/model_executor/models/nemotron_h.py
-6
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@@ -136,7 +136,6 @@ class NemotronHMoE(nn.Module):
self.routed_scaling_factor = config.routed_scaling_factor
self.ep_group = get_ep_group().device_group
self.ep_rank = self.ep_group.rank()
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.n_routed_experts
self.n_shared_experts: int = config.n_shared_experts
@@ -166,11 +165,6 @@ class NemotronHMoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
if config.n_shared_experts is None or config.n_shared_experts == 0:
self.shared_experts = None
else:
vllm/model_executor/models/openpangu.py
-6
View File
@@ -142,7 +142,6 @@ class OpenPanguMoE(nn.Module):
self.routed_scaling_factor = config.routed_scaling_factor
self.ep_group = get_ep_group().device_group
self.ep_rank = self.ep_group.rank()
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.n_routed_experts
self.n_shared_experts: int = config.n_shared_experts
@@ -176,11 +175,6 @@ class OpenPanguMoE(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
if config.n_shared_experts is not None:
intermediate_size = config.moe_intermediate_size * config.n_shared_experts
self.shared_experts = OpenPanguMLP(
vllm/model_executor/models/qwen3_moe.py
+3 -19
View File
@@ -142,7 +142,6 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -164,11 +163,6 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.gate = ReplicatedLinear(
config.hidden_size,
config.num_experts,
@@ -227,19 +221,9 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
if self.is_sequence_parallel:
hidden_states = sequence_parallel_chunk(hidden_states)
if self.experts.is_internal_router:
# In this case, the gate/router runs inside the MoERunner class
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
else:
# Actually this will be dead code, since we always pass gate into
# MoERunner in the current implementation. But we keep this code
# here for clarity and future flexibility.
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
if self.is_sequence_parallel:
final_hidden_states = tensor_model_parallel_all_gather(
vllm/model_executor/models/qwen3_next.py
+3 -17
View File
@@ -109,7 +109,6 @@ class Qwen3NextSparseMoeBlock(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.ep_group = get_ep_group().device_group
self.ep_rank = get_ep_group().rank_in_group
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.num_experts
@@ -130,11 +129,6 @@ class Qwen3NextSparseMoeBlock(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
self.gate = ReplicatedLinear(
config.hidden_size,
config.num_experts,
@@ -205,17 +199,9 @@ class Qwen3NextSparseMoeBlock(nn.Module):
if self.is_sequence_parallel and not already_sequence_parallel:
hidden_states = sequence_parallel_chunk(hidden_states)
if self.experts.is_internal_router:
# In this case, the gate/router runs inside the MoERunner class
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
else:
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
if self.is_sequence_parallel and not already_sequence_parallel:
final_hidden_states = tensor_model_parallel_all_gather(
vllm/model_executor/models/step3p5.py
+3 -16
View File
@@ -303,7 +303,6 @@ class FusedMoEBlock(nn.Module):
self.layer_idx = extract_layer_index(prefix)
self.ep_size = get_ep_group().device_group.size()
self.ep_rank = get_ep_group().device_group.rank()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
parallel_config = vllm_config.parallel_config
@@ -316,11 +315,6 @@ class FusedMoEBlock(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
self.physical_expert_end = (
self.physical_expert_start + self.n_local_physical_experts
)
if self.tp_size > config.moe_num_experts:
raise ValueError(
f"Tensor parallel size {self.tp_size} is greater than "
@@ -399,16 +393,9 @@ class FusedMoEBlock(nn.Module):
num_tokens, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
if self.experts.is_internal_router:
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
else:
# TODO(bnell): this gate could be moved into the MoERunner?
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=hidden_states
)
return final_hidden_states.view(num_tokens, hidden_dim)
vllm/models/deepseek_v4/amd/model.py
+5 -14
View File
@@ -307,20 +307,11 @@ class DeepseekV4MoE(nn.Module):
raise ValueError("DeepSeek V4 hash MoE routing requires input_ids.")
org_shape = hidden_states.shape
if self.experts.is_internal_router:
# In this case, the gate/router runs inside the MoERunner class
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
input_ids=input_ids,
)
else:
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=router_logits,
input_ids=input_ids,
)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
input_ids=input_ids,
)
return final_hidden_states.view(org_shape)
vllm/models/deepseek_v4/nvidia/model.py
+5 -14
View File
@@ -746,20 +746,11 @@ class DeepseekV4MoE(nn.Module):
self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None
) -> torch.Tensor:
org_shape = hidden_states.shape
if self.experts.is_internal_router:
# In this case, the gate/router runs inside the MoERunner class
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
input_ids=input_ids,
)
else:
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=router_logits,
input_ids=input_ids,
)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
input_ids=input_ids,
)
return final_hidden_states.view(org_shape)
vllm/models/deepseek_v4/xpu/model.py
+5 -13
View File
@@ -792,19 +792,11 @@ class DeepseekV4MoE(nn.Module):
self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None
) -> torch.Tensor:
org_shape = hidden_states.shape
if self.experts.is_internal_router:
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
input_ids=input_ids,
)
else:
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=router_logits,
input_ids=input_ids,
)
final_hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=hidden_states,
input_ids=input_ids,
)
return final_hidden_states.view(org_shape)