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adapter qwen3 moe attr for init (#3066)

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adapter qwen3 moe attr for init
This commit is contained in:
zhink
2025-07-30 16:49:28 +08:00
committed by GitHub
parent 8e203666d9
commit d89b6dd43f
1 changed files with 10 additions and 13 deletions
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23
fastdeploy/model_executor/models/qwen3moe.py
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@@ -97,29 +97,26 @@ class Qwen3DecoderLayer(nn.Layer):
prefix: str = "",
) -> None:
super().__init__()
layer_id = int(prefix.split(sep=".")[-1])
layer_id = int(prefix.split(sep=".")[-1])
self.self_attn = Qwen3Attention(
fd_config=fd_config,
layer_id=layer_id,
prefix=f"{prefix}.self_attn",
)
weight_key_map = {
"gate_weight_key": f"{prefix}.mlp.gate.weight",
"up_gate_proj_expert_weight_key": f"{prefix}.mlp.experts.{{}}.up_gate_proj.weight",
"down_proj_expert_weight_key": f"{prefix}.mlp.experts.{{}}.down_proj.weight",
}
if (
fd_config.model_config.moe_num_experts is not None
and layer_id >= fd_config.model_config.moe_layer_start_index
):
if fd_config.model_config.num_experts is not None and layer_id >= fd_config.model_config.moe_layer_start_index:
self.mlp = FusedMoE(
fd_config,
moe_intermediate_size=fd_config.model_config.moe_intermediate_size,
num_experts=fd_config.model_config.moe_num_experts,
top_k=fd_config.model_config.moe_topk,
num_experts=fd_config.model_config.num_experts,
top_k=fd_config.model_config.num_experts_per_tok,
layer_idx=layer_id,
weight_key_map=weight_key_map,
)
@@ -386,12 +383,12 @@ class Qwen3MoePretrainedModel(PretrainedModel):
return final_actions
num_experts = 0
if isinstance(config.moe_num_experts, list):
num_experts = sum(config.moe_num_experts)
elif isinstance(config.moe_num_experts, int):
num_experts = config.moe_num_experts
if isinstance(config.num_experts, list):
num_experts = sum(config.num_experts)
elif isinstance(config.num_experts, int):
num_experts = config.num_experts
else:
raise ValueError(f"Not support type of num_experts [{type(config.moe_num_experts)}]")
raise ValueError(f"Not support type of num_experts [{type(config.num_experts)}]")
mappings = get_tensor_parallel_split_mappings(config.num_hidden_layers, num_experts)