Support bailing moe (#8680)

docs/supported_models/generative_models.md

@@ -47,5 +47,6 @@ in the GitHub search bar.
 | **MiMo** (7B series)               | `XiaomiMiMo/MiMo-7B-RL`                         | Xiaomi's reasoning-optimized model series, leverages Multiple-Token Prediction for faster inference. |
 | **Arcee AFM-4.5B**               | `arcee-ai/AFM-4.5B-Base`                         | Arcee's foundational model series for real world reliability and edge deployments. |
 | **Persimmon** (8B)               | `adept/persimmon-8b-chat`                         | Adept’s open 8B model with a 16K context window and fast inference; trained for broad usability and licensed under Apache 2.0. |
+| **Ling** (16.8B–290B) | `inclusionAI/Ling-lite`, `inclusionAI/Ling-plus` | InclusionAI’s open MoE models. Ling-Lite has 16.8B total / 2.75B active parameters, and Ling-Plus has 290B total / 28.8B active parameters. They are designed for high performance on NLP and complex reasoning tasks. |
 | **Granite 3.0, 3.1** (IBM)               | `ibm-granite/granite-3.1-8b-instruct`                          | IBM's open dense foundation models optimized for reasoning, code, and business AI use cases. Integrated with Red Hat and watsonx systems. |
 | **Granite 3.0 MoE** (IBM)               | `ibm-granite/granite-3.0-3b-a800m-instruct`                          | IBM’s Mixture-of-Experts models offering strong performance with cost-efficiency. MoE expert routing designed for enterprise deployment at scale. |

python/sglang/srt/models/bailing_moe.py

@@ -0,0 +1,425 @@
+# Copyright 2023-2024 SGLang Team
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/bailing_moe.py
+
+from collections.abc import Iterable
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers.configuration_utils import PretrainedConfig
+
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.utils import add_prefix, make_layers
+
+
+class BailingAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = config.num_attention_heads
+        self.total_num_kv_heads = config.num_key_value_heads
+
+        assert self.total_num_heads % tp_size == 0
+        assert self.total_num_kv_heads % tp_size == 0
+
+        self.num_heads = self.total_num_heads // tp_size
+        self.head_dim = config.head_dim or (self.hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+
+        self.num_kv_heads = self.total_num_kv_heads // tp_size
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scale = self.head_dim**-0.5
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=(config.use_bias or config.use_qkv_bias),
+            quant_config=quant_config,
+            prefix=add_prefix("query_key_value", prefix),
+        )
+
+        self.dense = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("dense", prefix),
+        )
+
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scale,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=config.max_position_embeddings,
+            base=config.rope_theta,
+            is_neox_style=True,
+            rope_scaling=config.rope_scaling,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q, k = self.rotary_emb(position_ids, q, k)
+        context_layer = self.attn(q, k, v, forward_batch)
+        attn_output, _ = self.dense(context_layer)
+        return attn_output
+
+
+class BailingMLP(nn.Module):
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: Optional[bool] = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            config.hidden_size,
+            [intermediate_size] * 2,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            config.hidden_size,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=add_prefix("down_proj", prefix),
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        x, _ = self.gate_up_proj(x)
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class BailingMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+        self.hidden_size = config.hidden_size
+        self.num_shared_experts = config.num_shared_experts
+        self.norm_expert_prob = config.norm_topk_prob
+        self.moe_intermediate_size = config.moe_intermediate_size
+
+        self.gate = ReplicatedLinear(
+            self.hidden_size, self.num_experts, bias=False, quant_config=None
+        )
+
+        self.topk = TopK(top_k=self.top_k, renormalize=self.norm_expert_prob)
+
+        self.experts = FusedMoE(
+            num_experts=self.num_experts,
+            top_k=self.top_k,
+            layer_id=layer_id,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.moe_intermediate_size,
+            reduce_results=False,
+            quant_config=quant_config,
+            prefix=add_prefix("experts", prefix),
+        )
+
+        if self.num_shared_experts > 0:
+            shared_intermediate_size = (
+                self.moe_intermediate_size * self.num_shared_experts
+            )
+            self.shared_experts = BailingMLP(
+                intermediate_size=shared_intermediate_size,
+                config=config,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=add_prefix("shared_experts", prefix),
+            )
+        else:
+            self.shared_experts = None
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_states_flat = hidden_states.view(-1, self.hidden_size)
+
+        shared_output = None
+        if self.shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states_flat)
+
+        router_logits, _ = self.gate(hidden_states_flat)
+        topk_output = self.topk(hidden_states_flat, router_logits)
+        final_hidden_states = self.experts(hidden_states_flat, topk_output)
+
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+
+        return final_hidden_states.view(orig_shape)
+
+
+class BailingMoeBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attention = BailingAttention(
+            config, layer_id, quant_config, prefix=add_prefix("attention", prefix)
+        )
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.mlp = BailingMoE(
+            config=config,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        forward_batch: ForwardBatch,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Pre-normalization and residual connection for the attention block
+        if residual is None:
+            residual = hidden_states
+            normed_hidden_states = self.input_layernorm(hidden_states)
+        else:
+            normed_hidden_states, residual = self.input_layernorm(
+                hidden_states, residual
+            )
+
+        attn_output = self.attention(
+            hidden_states=normed_hidden_states,
+            position_ids=position_ids,
+            forward_batch=forward_batch,
+        )
+
+        # Pre-normalization and residual connection for the MLP block
+        normed_hidden_states, residual = self.post_attention_layernorm(
+            attn_output, residual
+        )
+        mlp_output = self.mlp(normed_hidden_states)
+
+        return mlp_output, residual
+
+
+class BailingMoeModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_dim = config.hidden_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            prefix=add_prefix("embed_tokens", prefix),
+        )
+        self.embedding_dropout = torch.nn.Dropout(config.embedding_dropout)
+
+        self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: BailingMoeBlock(
+                config=config,
+                layer_id=idx,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=add_prefix("layers", prefix),
+        )
+
+        self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+
+        residual = None
+        for layer in self.layers:
+            hidden_states, residual = layer(
+                hidden_states,
+                position_ids,
+                residual,
+                forward_batch,
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class BailingMoeForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.model = BailingMoeModel(config=config, quant_config=quant_config)
+        self.lm_head = ParallelLMHead(
+            num_embeddings=config.vocab_size,
+            embedding_dim=config.hidden_size,
+            quant_config=quant_config,
+        )
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+        self.logits_processor = LogitsProcessor(config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, forward_batch, inputs_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+
+        stacked_params_mapping = [
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+
+            if (
+                hasattr(self.config, "norm_head")
+                and self.config.norm_head
+                and "lm_head.weight" in name
+            ):
+                loaded_weight = F.normalize(loaded_weight, dim=0, p=2, eps=1e-7)
+
+            if "model.word_embeddings.weight" == name:
+                name = "model.embed_tokens.weight"
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name in name and "mlp.experts" not in name:
+                    full_param_name = name.replace(weight_name, param_name)
+                    param = params_dict[full_param_name]
+                    param.weight_loader(param, loaded_weight, shard_id)
+                    break
+            else:
+                for p_name, w_name, e_id, s_id in expert_params_mapping:
+                    if w_name in name and "mlp.experts" in name:
+                        full_param_name = name.replace(w_name, p_name)
+                        param = params_dict[full_param_name]
+                        param.weight_loader(
+                            param,
+                            loaded_weight,
+                            full_param_name,
+                            shard_id=s_id,
+                            expert_id=e_id,
+                        )
+                        break
+                else:
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+
+EntryClass = BailingMoeForCausalLM

test/srt/models/test_generation_models.py

@@ -67,6 +67,7 @@ ALL_MODELS = [
     ModelCase("openai-community/gpt2"),
     ModelCase("microsoft/phi-1_5", trust_remote_code=True),
     ModelCase("adept/persimmon-8b-chat"),
+    ModelCase("inclusionAI/Ling-lite", trust_remote_code=True),
     ModelCase("microsoft/Phi-3-small-8k-instruct", trust_remote_code=True),
     ModelCase("allenai/OLMo-2-1124-7B-Instruct", skip_long_prompt=True),
     ModelCase("ibm-granite/granite-3.0-2b-instruct", skip_long_prompt=True),