EngineX-Hygon/sglang
5
0
Fork 0
Code Issues Pull Requests Actions 7 Projects Releases Wiki Activity

Revert "[NVIDIA]Fix local_num_experts for EP (#8779)" (#8797)

Browse Source
This commit is contained in:
Yineng Zhang
2025-08-04 23:30:43 -07:00
committed by GitHub
parent 873f384a51
commit 5e91fed1c5
2 changed files with 2 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
python/sglang/srt/layers/moe/fused_moe_triton/layer.py
View File

@@ -200,8 +200,7 @@ class FusedMoE(torch.nn.Module):
self.quant_config = quant_config self.quant_config = quant_config
self.quant_method.create_weights( self.quant_method.create_weights(
layer=self, layer=self,
num_experts=self.num_experts, num_experts=self.num_local_experts,
num_local_experts=self.num_local_experts,
hidden_size=hidden_size, hidden_size=hidden_size,
# FIXME: figure out which intermediate_size to use # FIXME: figure out which intermediate_size to use
intermediate_size=self.intermediate_size_per_partition, intermediate_size=self.intermediate_size_per_partition,

3
python/sglang/srt/layers/quantization/modelopt_quant.py
View File

@@ -752,7 +752,6 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
self, self,
layer: torch.nn.Module, layer: torch.nn.Module,
num_experts: int, num_experts: int,
num_local_experts: int,
hidden_size: int, hidden_size: int,
intermediate_size_per_partition: int, intermediate_size_per_partition: int,
params_dtype: torch.dtype, params_dtype: torch.dtype,
@@ -766,7 +765,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
# TODO(ch-wan): check if this is needed # TODO(ch-wan): check if this is needed
layer.num_experts = num_experts layer.num_experts = num_experts
layer.num_local_experts = num_local_experts layer.num_local_experts = num_experts
layer.intermediate_size_per_partition = intermediate_size_per_partition layer.intermediate_size_per_partition = intermediate_size_per_partition
layer.params_dtype = params_dtype layer.params_dtype = params_dtype
layer.quant_config = self.quant_config layer.quant_config = self.quant_config