[Refactor] Drop model placement attributes and parameters nothing reads

python/sglang/srt/models/afmoe.py

@@ -158,7 +158,6 @@ def __init__(
     ):
         super().__init__()
         self.config = config
-        self.rank = get_parallel().tp_rank
         self.tp_size = get_parallel().tp_size
 
         self.n_routed_experts = getattr(config, "num_experts", None)

python/sglang/srt/models/bailing_moe.py

@@ -329,9 +329,6 @@ def __init__(
             )
         # dispatcher
         if get_moe_a2a_backend().is_deepep():
-            # TODO: we will support tp < ep in the future
-            self.ep_size = get_parallel().tp_size
-
             self.deepep_dispatcher = DeepEPDispatcher(
                 group=get_parallel().tp_group.device_group,
                 router_topk=self.top_k,
@@ -509,7 +506,6 @@ def __init__(
         self.hidden_size = config.hidden_size
         self.total_num_heads = config.num_attention_heads
         self.total_kv_heads = config.num_key_value_heads
-        self.dp_size = get_parallel().attn_dp_size
         attn_tp_rank = get_parallel().attn_tp_rank
         attn_tp_size = get_parallel().attn_tp_size
 
@@ -652,7 +648,6 @@ def __init__(
         hidden_size = config.hidden_size
 
         self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
-        self.dp_size = get_parallel().attn_dp_size
         self.attention = BailingMoEAttention(
             config,
             layer_id,
@@ -662,8 +657,6 @@ def __init__(
             alt_stream=alt_stream,
         )
         self.layer_id = layer_id
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
 
         self.is_layer_sparse = self._is_layer_sparse(
             config, layer_id=layer_id, is_nextn=is_nextn

python/sglang/srt/models/bailing_moe_linear.py

@@ -246,7 +246,6 @@ def __init__(
         self.layer_id = layer_id
 
         self.tp_size = get_parallel().tp_size
-        self.tp_rank = get_parallel().tp_rank
 
         self.top_k = config.num_experts_per_tok
         self.norm_expert_prob = getattr(config, "norm_topk_prob", False)

python/sglang/srt/models/bailing_moe_nextn.py

@@ -219,7 +219,6 @@ def __init__(
     ) -> None:
         nn.Module.__init__(self)
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.num_fused_shared_experts = 0
         is_bailing_moe_v3 = _is_bailing_moe_v3_config(config)

python/sglang/srt/models/bailing_moe_v3.py

@@ -317,7 +317,6 @@ def __init__(
         self.config = config
         self.swiglu_limit = swiglu_limit
         self.tp_size = tp_size if tp_size is not None else get_parallel().tp_size
-        self.tp_rank = tp_rank if tp_rank is not None else get_parallel().tp_rank
 
         self.intermediate_size = intermediate_size
         self.padded_intermediate_size = padded_intermediate_size or intermediate_size
@@ -465,10 +464,8 @@ def __init__(
         self.alt_stream = alt_stream
 
         self.tp_size = get_parallel().tp_size
-        self.tp_rank = get_parallel().tp_rank
         self.moe_ep_size = get_parallel().moe_ep_size
         self.moe_tp_size = get_parallel().moe_tp_size
-        self.moe_tp_rank = get_parallel().moe_tp_rank
 
         self.top_k = config.num_experts_per_tok
         self.norm_expert_prob = getattr(config, "norm_topk_prob", False)
@@ -1425,7 +1422,6 @@ def __init__(
         self.pp_group = get_parallel().pp_group
         self.config = config
         self.quant_config = quant_config
-        self.tp_size = get_parallel().tp_size
 
         # Determine num_fused_shared_experts
         self.determine_num_fused_shared_experts()

python/sglang/srt/models/dbrx.py

@@ -70,7 +70,6 @@ def __init__(
         prefix: str = "",
     ):
         super().__init__()
-        self.tp_size = get_parallel().tp_size
         self.num_total_experts = config.ffn_config.moe_num_experts
         self.d_model = config.d_model
         self.layer = ReplicatedLinear(
@@ -244,7 +243,6 @@ def __init__(
         )
 
         tp_world_size = get_parallel().tp_size
-        self.tp_size = tp_world_size
         assert self.total_num_heads % tp_world_size == 0
         self.num_heads = self.total_num_heads // tp_world_size
         if self.total_num_kv_heads >= tp_world_size:

python/sglang/srt/models/deepseek.py

@@ -115,7 +115,6 @@ def __init__(
     ):
         super().__init__()
         self.config = config
-        self.rank = get_parallel().tp_rank
         self.tp_size = get_parallel().tp_size
         self.n_routed_experts = config.n_routed_experts
         self.top_k = config.num_experts_per_tok

python/sglang/srt/models/deepseek_nextn.py

@@ -277,7 +277,6 @@ def __init__(
     ) -> None:
         nn.Module.__init__(self)
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         # if not set, model load will be broken in DeepseekV3ForCausalLM load_weights()
         self.pp_group = get_parallel().pp_group

python/sglang/srt/models/deepseek_v2.py

@@ -3241,7 +3241,6 @@ def __init__(
 
         self.pp_group = get_parallel().pp_group
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.determine_num_fused_shared_experts()
         self.use_dsa = is_deepseek_dsa(config)

python/sglang/srt/models/dots3_common/modeling.py

@@ -1834,7 +1834,6 @@ def __init__(
 
         self.pp_group = get_parallel().pp_group
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.determine_num_fused_shared_experts()
         self.model = Dots3Model(
@@ -2716,7 +2715,6 @@ def __init__(
         )
         language_model = self.thinker.language_model
         self.pp_group = language_model.pp_group
-        self.tp_size = language_model.tp_size
         self.quant_config = language_model.quant_config
         self.num_fused_shared_experts = language_model.num_fused_shared_experts
         self.forward = self.thinker.forward

python/sglang/srt/models/dots3_common/nextn.py

@@ -146,7 +146,6 @@ def __init__(
     ) -> None:
         nn.Module.__init__(self)
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.pp_group = get_parallel().pp_group
         self.fuse_qkv_a_g_proj = True

python/sglang/srt/models/exaone4.py

@@ -244,9 +244,6 @@ def __init__(
 
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
 
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
-
         self.self_attn = Exaone4Attention(
             config=config,
             hidden_size=self.hidden_size,

python/sglang/srt/models/exaone_moe.py

@@ -134,7 +134,6 @@ def __init__(
     ):
         super().__init__()
         self.tp_size = get_parallel().tp_size
-        self.moe_ep_size = get_parallel().moe_ep_size
         self.layer_id = layer_id
         self.routed_scaling_factor = config.routed_scaling_factor
         self.alt_stream = alt_stream
@@ -200,7 +199,6 @@ def __init__(
             )
 
         if get_moe_a2a_backend().is_deepep():
-            self.ep_size = get_parallel().moe_ep_size
             self.num_experts = (
                 config.num_experts + get_exec().moe.ep_num_redundant_experts
             )
@@ -450,8 +448,6 @@ def __init__(
         attention_bias = getattr(config, "attention_bias", False) or getattr(
             config, "bias", False
         )
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
 
         self.self_attn = ExaoneMoEAttention(
             config=config,

python/sglang/srt/models/exaone_moe_mtp.py

@@ -45,7 +45,6 @@ def __init__(
         nn.Module.__init__(self)
         self.config = config
         config.num_hidden_layers = 1
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.pp_group = get_parallel().pp_group
 

python/sglang/srt/models/gemma4_causal.py

@@ -224,7 +224,6 @@ def __init__(
         self.layer_id = layer_id
         self.hidden_size = hidden_size
         self.num_experts = config.num_experts
-        self.tp_size = get_parallel().tp_size
 
         # Per-expert output scale folded into routing weights so that
         # MoE's fused kernel computes: Σ_e (expert_e * w_e * scale_e)

python/sglang/srt/models/gigachat35.py

@@ -598,7 +598,6 @@ def __init__(
         self.config = config
         self.quant_config = quant_config
         self.pp_group = get_parallel().pp_group
-        self.tp_size = get_parallel().tp_size
         self.num_fused_shared_experts = 0
 
         self.model = GigaChat35Model(

python/sglang/srt/models/gigachat35_mtp.py

@@ -141,7 +141,6 @@ def __init__(
         self.config = config
         self.quant_config = quant_config
         self.pp_group = get_parallel().pp_group
-        self.tp_size = get_parallel().tp_size
         self.num_fused_shared_experts = 0
         self.draft_model_idx = draft_model_idx or 0
 

python/sglang/srt/models/glm4_moe.py

@@ -258,7 +258,6 @@ def __init__(
         self.rope_theta = rope_theta
         self.use_qk_norm = use_qk_norm
         self.max_position_embeddings = max_position_embeddings
-        self.tp_rank = get_parallel().tp_rank
 
         self.qkv_proj = QKVParallelLinear(
             hidden_size,
@@ -431,7 +430,6 @@ def __init__(
         nn.Module.__init__(self)
         self.top_k = config.num_experts_per_tok
         self.tp_size = get_parallel().tp_size
-        self.moe_ep_size = get_parallel().moe_ep_size
         self.routed_scaling_factor = config.routed_scaling_factor
         self.n_shared_experts = config.n_shared_experts
         self.num_fused_shared_experts = (
@@ -1141,7 +1139,6 @@ def __init__(
         nn.Module.__init__(self)
         self.pp_group = get_parallel().pp_group
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.num_fused_shared_experts = 0
         self.determine_num_fused_shared_experts()

python/sglang/srt/models/glm4_moe_lite.py

@@ -850,7 +850,6 @@ def __init__(
         super().__init__()
         config.moe_layer_freq = 1
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.pp_group = get_parallel().pp_group
         self.determine_num_fused_shared_experts()

python/sglang/srt/models/glm4_moe_lite_nextn.py

@@ -142,7 +142,6 @@ def __init__(
     ) -> None:
         nn.Module.__init__(self)
         self.config = config
-        self.tp_size = get_parallel().tp_size
         if is_npu() and get_spec().speculative_draft_model_quantization is None:
             quant_config = None
         self.quant_config = quant_config

python/sglang/srt/models/glm4_moe_nextn.py

@@ -123,7 +123,6 @@ def __init__(
     ) -> None:
         nn.Module.__init__(self)
         self.config = config
-        self.tp_size = get_parallel().tp_size
         if is_npu() and get_spec().speculative_draft_model_quantization is None:
             quant_config = None
         self.quant_config = quant_config

python/sglang/srt/models/glm4v_moe.py

@@ -43,7 +43,6 @@ def __init__(
         self.config = config
         self.use_data_parallel = get_mm().mm_enable_dp_encoder
         vision_utils.update_vit_attn_dummy_heads_config(self.config)
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.num_fused_shared_experts = 0
         self.determine_num_fused_shared_experts()

python/sglang/srt/models/glm5_next.py

@@ -391,7 +391,6 @@ def __init__(
         **kwargs,
     ) -> None:
         super().__init__()
-        self.tp_size = get_parallel().tp_size
         head_shard_size = get_parallel().attn_tp_size
         head_shard_rank = get_parallel().attn_tp_rank
         _head_shard_rank_getter = partial(getattr, get_parallel(), "attn_tp_rank")
@@ -1237,7 +1236,6 @@ def __init__(
 
         self.pp_group = get_parallel().pp_group
         self.config = text_config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         self.use_dsa = is_deepseek_dsa(text_config)
         self.num_fused_shared_experts = 0

python/sglang/srt/models/glm_ocr_nextn.py

@@ -137,7 +137,6 @@ def __init__(
     ) -> None:
         nn.Module.__init__(self)
         self.config = config
-        self.tp_size = get_parallel().tp_size
         self.quant_config = quant_config
         _, rope_scaling = get_rope_config(config)
         self.is_mrope_enabled = "mrope_section" in (rope_scaling or {})

python/sglang/srt/models/gpt_oss.py

@@ -209,7 +209,6 @@ def __init__(
         prefix: str = "",
     ):
         super().__init__()
-        self.tp_size = get_parallel().tp_size
         self.layer_id = layer_id
         self.hidden_size = config.hidden_size
         self.activation = config.hidden_act
@@ -408,7 +407,6 @@ def __init__(
         self.scaling = self.head_dim**-0.5
         self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
-        self.tp_rank = get_parallel().tp_rank
 
         self.qkv_proj = QKVParallelLinear(
             hidden_size,
@@ -563,9 +561,6 @@ def __init__(
 
         self.layer_id = layer_id
 
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
-
         # GptOss all layers are sparse
         self.is_layer_sparse = True
         is_previous_layer_sparse = True

python/sglang/srt/models/grok.py

@@ -118,7 +118,6 @@ def __init__(
         intermediate_size: int,
         params_dtype: Optional[torch.dtype] = None,
         quant_config: Optional[QuantizationConfig] = None,
-        tp_size: Optional[int] = None,
         reduce_results: bool = True,
         use_presharded_weights: bool = False,
         inplace: bool = True,

python/sglang/srt/models/hunyuan_v3.py

@@ -95,8 +95,6 @@ def __init__(
         alt_stream: Optional[torch.cuda.Stream] = None,
     ):
         super().__init__()
-        self.tp_size = get_parallel().moe_tp_size
-        self.ep_size = get_parallel().moe_ep_size
         self.layer_id = layer_id
         self.alt_stream = alt_stream
         self.n_routed_experts = config.num_experts

python/sglang/srt/models/interns2_mobius.py

@@ -323,7 +323,6 @@ def __init__(
     ) -> None:
         super().__init__()
         self.bank_id = bank_id
-        self.tp_size = get_parallel().tp_size
         self.num_experts = config.num_experts
         self.topk = TopK(
             top_k=config.num_experts_per_tok,

python/sglang/srt/models/llada2.py

@@ -303,9 +303,6 @@ def __init__(
             )
         # dispatcher
         if get_moe_a2a_backend().is_deepep():
-            # TODO: we will support tp < ep in the future
-            self.ep_size = get_parallel().tp_size
-
             self.deepep_dispatcher = DeepEPDispatcher(
                 group=get_parallel().tp_group.device_group,
                 router_topk=self.top_k,
@@ -432,7 +429,6 @@ def __init__(
         self.hidden_size = config.hidden_size
         self.total_num_heads = config.num_attention_heads
         self.total_kv_heads = config.num_key_value_heads
-        self.dp_size = get_parallel().attn_dp_size
         attn_tp_rank = get_parallel().attn_tp_rank
         attn_tp_size = get_parallel().attn_tp_size
 
@@ -598,7 +594,6 @@ def __init__(
         hidden_size = config.hidden_size
 
         self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
-        self.dp_size = get_parallel().attn_dp_size
         self.attention = LLaDA2MoeAttention(
             config,
             layer_id,
@@ -608,8 +603,6 @@ def __init__(
             alt_stream=alt_stream,
         )
         self.layer_id = layer_id
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
 
         self.is_layer_sparse = self._is_layer_sparse(config, layer_id=layer_id)
         is_previous_layer_sparse = self._is_layer_sparse(config, layer_id=layer_id - 1)

python/sglang/srt/models/llama4.py

@@ -98,7 +98,6 @@ def __init__(
         prefix: str = "",
     ):
         super().__init__()
-        self.tp_size = get_parallel().tp_size
         self.top_k = config.num_experts_per_tok
         self.device_module = torch.get_device_module()
 
@@ -385,8 +384,6 @@ def __init__(
         rope_theta = config.rope_parameters["rope_theta"]
         rope_scaling = config.rope_parameters
         max_position_embeddings = config.max_position_embeddings
-        self.attn_tp_size = get_parallel().attn_tp_size
-        self.attn_tp_rank = get_parallel().attn_tp_rank
 
         self.self_attn = Llama4Attention(
             config=config,