Support XiaomiMiMo inference with mtp (#6059)

python/sglang/srt/configs/model_config.py

@@ -73,6 +73,7 @@ class ModelConfig:
             model_override_args=self.model_override_args,
             **kwargs,
         )
+
         self.hf_text_config = get_hf_text_config(self.hf_config)
         self.attention_chunk_size = getattr(
             self.hf_text_config, "attention_chunk_size", None
@@ -97,6 +98,8 @@ class ModelConfig:
         ):
             self.hf_config.architectures[0] = "DeepseekV3ForCausalLMNextN"
 
+        if is_draft_model and self.hf_config.architectures[0] == "MiMoForCausalLM":
+            self.hf_config.architectures[0] = "MiMoMTP"
         # Check model type
         self.is_generation = is_generation_model(
             self.hf_config.architectures, is_embedding

python/sglang/srt/model_executor/model_runner.py

@@ -782,12 +782,15 @@ class ModelRunner:
             distributed=get_world_group().world_size > 1,
             cpu_group=get_world_group().cpu_group,
         )
-        if self.use_mla_backend:
-            num_layers = (
-                self.model_config.num_hidden_layers
-                if not self.is_draft_worker
-                else self.model_config.hf_config.num_nextn_predict_layers
+        if self.is_draft_worker:
+            num_layers = getattr(
+                self.model_config.hf_config,
+                "num_nextn_predict_layers",
+                self.num_effective_layers,
             )
+        else:
+            num_layers = self.num_effective_layers
+        if self.use_mla_backend:
             # FIXME: pipeline parallelism is not compatible with mla backend
             assert self.pp_size == 1
             cell_size = (
@@ -799,7 +802,7 @@ class ModelRunner:
             cell_size = (
                 self.model_config.get_num_kv_heads(get_attention_tp_size())
                 * self.model_config.head_dim
-                * self.num_effective_layers
+                * num_layers
                 * 2
                 * torch._utils._element_size(self.kv_cache_dtype)
             )

python/sglang/srt/models/mimo_mtp.py

@@ -0,0 +1,220 @@
+# Adapted from https://github.com/vllm-project/vllm/pull/17433/files  and deepseek_nextn.py
+
+from functools import partial
+from typing import Any, Dict, Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    split_tensor_along_last_dim,
+    tensor_model_parallel_all_gather,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import QKVParallelLinear, RowParallelLinear
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+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.models.mimo import MiMoForCausalLM
+from sglang.srt.models.qwen2 import (
+    Qwen2Attention,
+    Qwen2DecoderLayer,
+    Qwen2MLP,
+    Qwen2Model,
+)
+from sglang.srt.utils import add_prefix
+
+
+class MiMoMultiTokenPredictorLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.token_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hidden_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_proj = nn.Linear(
+            config.hidden_size * 2, config.hidden_size, bias=False
+        )
+        self.mtp_block = Qwen2DecoderLayer(
+            config=config, quant_config=quant_config, prefix=prefix
+        )
+        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.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+        # masking inputs at position 0, as not needed by MTP
+        hidden_states[positions == 0] = 0
+
+        hidden_states = self.input_proj(
+            torch.cat(
+                (
+                    self.hidden_layernorm(forward_batch.spec_info.hidden_states),
+                    self.token_layernorm(hidden_states),
+                ),
+                dim=-1,
+            )
+        )
+
+        hidden_states, residual = self.mtp_block(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+            residual=None,
+        )
+        hidden_states = residual + hidden_states
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+class MiMoMTP(nn.Module):
+    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
+
+        self.model = MiMoMultiTokenPredictorLayer(
+            config,
+            prefix,
+            quant_config,
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+        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]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name or "projector" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            if name.startswith("model.vision_tower") and name not in params_dict:
+                continue
+            name = self.map_model_name_to_mtp_param_name(name)
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mtp_block" not in name:
+                    break
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if "mtp_block" not in name and (
+                    "embed_tokens" not in name
+                    and "lm_head" not in name
+                    and "token_layernorm" not in name
+                    and "hidden_layernorm" not in name
+                    and "input_proj" not in name
+                    and "final_layernorm" not in name
+                ):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+    def map_model_name_to_mtp_param_name(self, name: str) -> str:
+        import re
+
+        name_without_prefix = [
+            "token_layernorm",
+            "hidden_layernorm",
+            "input_proj",
+            "final_layernorm",
+        ]
+        pattern = r"model.mtp_layers.(\d+)."
+        group = re.match(pattern, name)
+        if group is not None:
+            for sub_name in name_without_prefix:
+                if sub_name in name:
+                    name = name.replace(group.group(), "model.")
+                    return name
+            name = name.replace(group.group(), "model.mtp_block.")
+        return name
+
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        del self.model.embed_tokens.weight
+        del self.lm_head.weight
+        self.model.embed_tokens.weight = embed
+        self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+
+EntryClass = MiMoMTP