Support LingV2 model (#10359)

Co-authored-by: 羽癫 <yudian.zy@antgroup.com>
Co-authored-by: guoyuhong <yuhong.gyh@antgroup.com>

python/sglang/srt/configs/model_config.py

@@ -141,6 +141,11 @@ class ModelConfig:
 
         if is_draft_model and self.hf_config.architectures[0] == "MiMoForCausalLM":
             self.hf_config.architectures[0] = "MiMoMTP"
+        if is_draft_model and self.hf_config.architectures[0] in [
+            "BailingMoeV2ForCausalLM",
+            "BailingMoeForCausalLM",
+        ]:
+            self.hf_config.architectures[0] = "BailingMoeForCausalLMNextN"
         if (
             is_draft_model
             and self.hf_config.architectures[0] == "Ernie4_5_MoeForCausalLM"

python/sglang/srt/layers/linear.py

@@ -893,6 +893,35 @@ class QKVParallelLinear(ColumnParallelLinear):
                 )
             self.weight_loader_v2(param, loaded_weight_shard, shard_id)
 
+    def _load_qkv_block_scale(
+        self, param: BasevLLMParameter, loaded_weight: torch.Tensor
+    ):
+        block_n, _ = self.quant_method.quant_config.weight_block_size
+        q_size = self.total_num_heads * self.head_size // block_n
+        k_size = self.total_num_kv_heads * self.head_size // block_n
+        v_size = self.total_num_kv_heads * self.head_size // block_n
+        shard_offsets = [
+            # (shard_id, shard_offset, shard_size)
+            ("q", 0, q_size),
+            ("k", q_size, k_size),
+            ("v", q_size + k_size, v_size),
+        ]
+        for shard_id, shard_offset, shard_size in shard_offsets:
+            loaded_weight_shard = loaded_weight.narrow(
+                param.output_dim, shard_offset, shard_size
+            )
+            rank_shard_offset = self._get_shard_offset_mapping(shard_id) // block_n
+            rank_shard_size = self._get_shard_size_mapping(shard_id) // block_n
+            param.load_qkv_weight(
+                loaded_weight=loaded_weight_shard,
+                num_heads=self.num_kv_head_replicas,
+                shard_id=shard_id,
+                shard_offset=rank_shard_offset,
+                shard_size=rank_shard_size,
+                tp_rank=self.tp_rank,
+                use_presharded_weights=self.use_presharded_weights,
+            )
+
     def weight_loader_v2(
         self,
         param: BasevLLMParameter,
@@ -906,6 +935,9 @@ class QKVParallelLinear(ColumnParallelLinear):
             elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                 param.load_qkv_weight(loaded_weight=loaded_weight)
                 return
+            elif isinstance(param, BlockQuantScaleParameter):
+                self._load_qkv_block_scale(param, loaded_weight)
+                return
             # TODO: @dsikka - move to parameter.py
             self._load_fused_module_from_checkpoint(param, loaded_weight)
             return

python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_4_0/E=256,N=512,device_name=NVIDIA_H20.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 5
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 5
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 5
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 5
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

python/sglang/srt/models/bailing_moe_nextn.py

@@ -0,0 +1,168 @@
+# coding=utf-8
+# Copyright 2023 Antgroup and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" SGLang BailingMoENextN model."""
+import logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.topk import select_experts
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.bailing_moe import BailingMoEBlock, BailingMoEForCausalLM
+from sglang.srt.utils import add_prefix
+
+LoraConfig = None
+logger = logging.getLogger(__name__)
+
+
+class BailingMoEModelNextN(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if quant_config is not None and quant_config.get_name() == "modelopt_fp4":
+            logger.warning(
+                "Overriding DeepseekV3ForCausalLMNextN quant config for modelopt_fp4 Deepseek model."
+            )
+            quant_config = None
+
+        self.vocab_size = config.vocab_size
+
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            enable_tp=not is_dp_attention_enabled(),
+            prefix=add_prefix("word_embeddings", prefix),
+        )
+
+        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.eh_proj = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
+
+        self.decoder = BailingMoEBlock(
+            config,
+            0,
+            quant_config=quant_config,
+            # is_nextn=True,
+            prefix=add_prefix("decoder", prefix),
+        )
+
+        self.shared_head = nn.Module()
+        self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+
+        if input_embeds is None:
+            hidden_states = self.word_embeddings(input_ids)
+        else:
+            hidden_states = input_embeds
+
+        if hidden_states.shape[0] > 0:
+            hidden_states = self.eh_proj(
+                torch.cat(
+                    (
+                        self.enorm(hidden_states),
+                        self.hnorm(forward_batch.spec_info.hidden_states),
+                    ),
+                    dim=-1,
+                )
+            )
+
+        residual = None
+        hidden_states, residual = self.decoder(
+            positions, hidden_states, forward_batch, residual
+        )
+
+        if not forward_batch.forward_mode.is_idle():
+            if residual is not None:
+                hidden_states, _ = self.final_layernorm(hidden_states, residual)
+            else:
+                hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+class BailingMoeForCausalLMNextN(BailingMoEForCausalLM):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.quant_config = quant_config
+        if hasattr(self, "determine_num_fused_shared_experts"):
+            # Asystem has determine_num_fused_shared_experts but theta does not.
+            self.determine_num_fused_shared_experts("BailingMoeForCausalLMNextN")
+
+        self.model = BailingMoEModelNextN(
+            config, quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=add_prefix("model.shared_head.head", prefix),
+            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        super().load_weights(weights, is_nextn=True)
+
+
+EntryClass = [BailingMoeForCausalLMNextN]

python/sglang/srt/server_args.py

@@ -754,7 +754,12 @@ class ServerArgs:
                 )
 
             model_arch = self.get_hf_config().architectures[0]
-            if model_arch in ["DeepseekV3ForCausalLM", "Glm4MoeForCausalLM"]:
+            if model_arch in [
+                "DeepseekV3ForCausalLM",
+                "Glm4MoeForCausalLM",
+                "BailingMoeV2ForCausalLM",
+                "BailingMoeV2ForCausalLM",
+            ]:
                 # Auto set draft_model_path DeepSeek-V3/R1
                 if self.speculative_draft_model_path is None:
                     self.speculative_draft_model_path = self.model_path
@@ -2724,6 +2729,8 @@ def auto_choose_speculative_params(self: ServerArgs):
         "DeepseekV3ForCausalLM",
         "DeepseekV2ForCausalLM",
         "GptOssForCausalLM",
+        "BailingMoeForCausalLM",
+        "BailingMoeV2ForCausalLM",
     ]:
         # The default value for deepseek and gpt-oss
         return (3, 1, 4)