[Model] Add MiMo-V2-Flash support (#30836)

Signed-off-by: Abatom <abzhonghua@gmail.com>
Signed-off-by: Jumiar <liuanqim10@126.com>
Signed-off-by: Zyann7 <zyann7@outlook.com>
Co-authored-by: Jumiar <liuanqim10@126.com>
Co-authored-by: Zyann7 <zyann7@outlook.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>

docs/models/supported_models.md

@@ -415,6 +415,7 @@ th {
 | `MambaForCausalLM` | Mamba | `state-spaces/mamba-130m-hf`, `state-spaces/mamba-790m-hf`, `state-spaces/mamba-2.8b-hf`, etc. | | ✅︎ |
 | `Mamba2ForCausalLM` | Mamba2 | `mistralai/Mamba-Codestral-7B-v0.1`, etc. | | ✅︎ |
 | `MiMoForCausalLM` | MiMo | `XiaomiMiMo/MiMo-7B-RL`, etc. | ✅︎ | ✅︎ |
+| `MiMoV2FlashForCausalLM` | MiMoV2Flash | `XiaomiMiMo/MiMo-V2-Flash`, etc. | ︎| ✅︎ |
 | `MiniCPMForCausalLM` | MiniCPM | `openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, `openbmb/MiniCPM-S-1B-sft`, etc. | ✅︎ | ✅︎ |
 | `MiniCPM3ForCausalLM` | MiniCPM3 | `openbmb/MiniCPM3-4B`, etc. | ✅︎ | ✅︎ |
 | `MiniMaxM2ForCausalLM` | MiniMax-M2 |`MiniMaxAI/MiniMax-M2`, etc. | | ✅︎ |

tests/models/registry.py

@@ -459,6 +459,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     ),
     "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct"),
     "MiMoForCausalLM": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL", trust_remote_code=True),
+    "MiMoV2FlashForCausalLM": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True
+    ),
     "Dots1ForCausalLM": _HfExamplesInfo("rednote-hilab/dots.llm1.inst"),
 }
 

vllm/config/__init__.py

@@ -18,6 +18,7 @@ from vllm.config.lora import LoRAConfig
 from vllm.config.model import (
     ModelConfig,
     iter_architecture_defaults,
+    str_dtype_to_torch_dtype,
     try_match_architecture_defaults,
 )
 from vllm.config.multimodal import MultiModalConfig
@@ -72,6 +73,7 @@ __all__ = [
     # From vllm.config.model
     "ModelConfig",
     "iter_architecture_defaults",
+    "str_dtype_to_torch_dtype",
     "try_match_architecture_defaults",
     # From vllm.config.multimodal
     "MultiModalConfig",

vllm/config/model.py

@@ -1849,6 +1849,11 @@ _STR_DTYPE_TO_TORCH_DTYPE = {
     "bfloat16": torch.bfloat16,
 }
 
+
+def str_dtype_to_torch_dtype(type: str):
+    return _STR_DTYPE_TO_TORCH_DTYPE.get(type)
+
+
 # model_type -> reason
 _FLOAT16_NOT_SUPPORTED_MODELS = {
     "gemma2": "Numerical instability. Please use bfloat16 or float32 instead.",

vllm/model_executor/layers/linear.py

@@ -277,6 +277,7 @@ class LinearBase(CustomOp):
         self.params_dtype = params_dtype
         self.quant_config = quant_config
         self.prefix = prefix
+        self.allow_fp8_block_shape_mismatch = False
         if quant_config is None:
             self.quant_method: QuantizeMethodBase | None = UnquantizedLinearMethod()
         else:
@@ -475,6 +476,7 @@ class ColumnParallelLinear(LinearBase):
             disable_tp=disable_tp,
         )
 
+        self._maybe_allow_fp8_block_shape_mismatch()
         self.gather_output = gather_output
 
         if output_sizes is None:
@@ -509,6 +511,33 @@ class ColumnParallelLinear(LinearBase):
             self.register_parameter("bias", None)
         self.update_param_tp_status()
 
+    def _maybe_allow_fp8_block_shape_mismatch(self) -> None:
+        quant_config = getattr(self, "quant_config", None)
+        weight_block = getattr(quant_config, "weight_block_size", None)
+        if (
+            weight_block is None
+            or len(weight_block) < 1
+            or len(self.output_partition_sizes) <= 1
+        ):
+            return
+
+        try:
+            block_n = int(weight_block[0])
+        except (ValueError, TypeError):
+            return
+
+        if block_n <= 0:
+            return
+
+        if any(size % block_n != 0 for size in self.output_partition_sizes):
+            self.allow_fp8_block_shape_mismatch = True
+            logger.debug(
+                "Allowing FP8 block shape mismatch for %s (block_n=%d, partitions=%s)",
+                getattr(self, "prefix", "<unknown>"),
+                block_n,
+                self.output_partition_sizes,
+            )
+
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         output_dim = getattr(param, "output_dim", None)
 
@@ -906,9 +935,11 @@ class QKVParallelLinear(ColumnParallelLinear):
         *,
         return_bias: bool = True,
         disable_tp: bool = False,
+        v_head_size: int | None = None,
     ):
         self.hidden_size = hidden_size
         self.head_size = head_size
+        self.v_head_size = v_head_size if v_head_size is not None else head_size
         self.total_num_heads = total_num_heads
         if total_num_kv_heads is None:
             total_num_kv_heads = total_num_heads
@@ -924,12 +955,14 @@ class QKVParallelLinear(ColumnParallelLinear):
             self.num_kv_head_replicas = 1
         input_size = self.hidden_size
         output_size = (
-            (self.num_heads + 2 * self.num_kv_heads) * tp_size * self.head_size
-        )
+            self.num_heads * self.head_size
+            + self.num_kv_heads * self.head_size
+            + self.num_kv_heads * self.v_head_size
+        ) * tp_size
         self.output_sizes = [
             self.num_heads * self.head_size * tp_size,  # q_proj
             self.num_kv_heads * self.head_size * tp_size,  # k_proj
-            self.num_kv_heads * self.head_size * tp_size,  # v_proj
+            self.num_kv_heads * self.v_head_size * tp_size,  # v_proj
         ]
 
         super().__init__(
@@ -950,7 +983,8 @@ class QKVParallelLinear(ColumnParallelLinear):
             "q": 0,
             "k": self.num_heads * self.head_size,
             "v": (self.num_heads + self.num_kv_heads) * self.head_size,
-            "total": (self.num_heads + 2 * self.num_kv_heads) * self.head_size,
+            "total": (self.num_heads + self.num_kv_heads) * self.head_size
+            + self.num_kv_heads * self.v_head_size,
         }
         return shard_offset_mapping.get(loaded_shard_id)
 
@@ -958,7 +992,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         shard_size_mapping = {
             "q": self.num_heads * self.head_size,
             "k": self.num_kv_heads * self.head_size,
-            "v": self.num_kv_heads * self.head_size,
+            "v": self.num_kv_heads * self.v_head_size,
         }
         return shard_size_mapping.get(loaded_shard_id)
 
@@ -985,7 +1019,7 @@ class QKVParallelLinear(ColumnParallelLinear):
             (
                 "v",
                 (self.total_num_heads + self.total_num_kv_heads) * self.head_size,
-                self.total_num_kv_heads * self.head_size,
+                self.total_num_kv_heads * self.v_head_size,
             ),
         ]
 
@@ -1110,7 +1144,7 @@ class QKVParallelLinear(ColumnParallelLinear):
                 (
                     "v",
                     (self.total_num_heads + self.total_num_kv_heads) * self.head_size,
-                    self.total_num_kv_heads * self.head_size,
+                    self.total_num_kv_heads * self.v_head_size,
                 ),
             ]
             use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
@@ -1139,11 +1173,12 @@ class QKVParallelLinear(ColumnParallelLinear):
                         "v": (
                             (self.total_num_heads + self.total_num_kv_heads)
                             * self.head_size,
-                            self.total_num_kv_heads * self.head_size,
+                            self.total_num_kv_heads * self.v_head_size,
                         ),
                         "total": (
-                            (self.total_num_heads + 2 * self.total_num_kv_heads)
-                            * self.head_size,
+                            (self.total_num_heads + self.total_num_kv_heads)
+                            * self.head_size
+                            + self.total_num_kv_heads * self.v_head_size,
                             0,
                         ),
                     }
@@ -1170,7 +1205,7 @@ class QKVParallelLinear(ColumnParallelLinear):
                 shard_size = self.num_kv_heads * self.head_size
             elif loaded_shard_id == "v":
                 shard_offset = (self.num_heads + self.num_kv_heads) * self.head_size
-                shard_size = self.num_kv_heads * self.head_size
+                shard_size = self.num_kv_heads * self.v_head_size
             # Special case for Quantized Weights.
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
@@ -1199,10 +1234,11 @@ class QKVParallelLinear(ColumnParallelLinear):
                     ),
                     "v": (
                         (self.num_heads + self.num_kv_heads) * self.head_size,
-                        self.num_kv_heads * self.head_size,
+                        self.num_kv_heads * self.v_head_size,
                     ),
                     "total": (
-                        (self.num_heads + 2 * self.num_kv_heads) * self.head_size,
+                        (self.num_heads + self.num_kv_heads) * self.head_size
+                        + self.num_kv_heads * self.v_head_size,
                         0,
                     ),
                 }

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -1252,6 +1252,14 @@ def validate_fp8_block_shape(
     """Validate block quantization shapes for tensor parallelism."""
     from vllm.distributed import get_tensor_model_parallel_world_size
 
+    if getattr(layer, "allow_fp8_block_shape_mismatch", False):
+        logger.debug(
+            "Skipping FP8 block shape validation for layer %s due to detected"
+            " mismatch allowance.",
+            getattr(layer, "prefix", "<unknown>"),
+        )
+        return
+
     tp_size = getattr(layer, "tp_size", get_tensor_model_parallel_world_size())
     block_n, block_k = block_size[0], block_size[1]
 

vllm/model_executor/models/mimo_v2_flash.py

@@ -0,0 +1,720 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
+from itertools import islice
+
+import torch
+from torch import nn
+
+from vllm.attention.backends.abstract import AttentionType
+from vllm.attention.layer import Attention
+from vllm.config import (
+    CacheConfig,
+    VllmConfig,
+    get_current_vllm_config,
+    str_dtype_to_torch_dtype,
+)
+from vllm.distributed import (
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_gather,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.model_executor.models.utils import sequence_parallel_chunk
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MixtureOfExperts, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class MiMoV2MLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MiMoV2MoE(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        is_nextn: bool = False,
+    ):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_text_config
+        parallel_config = vllm_config.parallel_config
+        quant_config = vllm_config.quant_config
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = get_ep_group().rank_in_group
+        self.ep_size = self.ep_group.size()
+        self.n_routed_experts = config.n_routed_experts
+
+        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
+
+        if self.tp_size > config.n_routed_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.n_routed_experts}."
+            )
+
+        if config.hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {config.hidden_act}. "
+                "Only silu is supported for now."
+            )
+
+        vllm_config = get_current_vllm_config()
+        eplb_config = vllm_config.parallel_config.eplb_config
+        self.enable_eplb = parallel_config.enable_eplb
+
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
+        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+
+        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
+        self.physical_expert_end = (
+            self.physical_expert_start + self.n_local_physical_experts
+        )
+
+        dtype = getattr(config, "moe_router_dtype", "float32")
+        self.gate_dtype = str_dtype_to_torch_dtype(dtype)
+        self.gate = nn.Linear(
+            config.hidden_size,
+            config.n_routed_experts,
+            bias=False,
+            dtype=self.gate_dtype,
+        )
+        self.gate.e_score_correction_bias = nn.Parameter(
+            torch.empty(config.n_routed_experts, dtype=self.gate_dtype)
+        )
+
+        self.experts = FusedMoE(
+            num_experts=self.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=True,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+            e_score_correction_bias=self.gate.e_score_correction_bias,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+            is_sequence_parallel=self.is_sequence_parallel,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            topk_group=config.topk_group,
+            scoring_func="sigmoid",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        assert hidden_states.dim() <= 2, "MiMoV2MoE only supports 1D or 2D inputs"
+        is_input_1d = hidden_states.dim() == 1
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        if self.is_sequence_parallel:
+            hidden_states = sequence_parallel_chunk(hidden_states)
+
+        if self.gate_dtype is not None:
+            gate_input = hidden_states.to(self.gate_dtype)
+        else:
+            gate_input = hidden_states
+        router_logits = self.gate(gate_input)
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
+        )
+
+        if self.is_sequence_parallel:
+            final_hidden_states = tensor_model_parallel_all_gather(
+                final_hidden_states, 0
+            )
+            final_hidden_states = final_hidden_states[:num_tokens]
+
+        return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states
+
+
+class MiMoV2Attention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        v_head_dim: int | None = None,
+        sliding_window_size: int = -1,
+        attention_bias: bool = False,
+        add_swa_attention_sink_bias: bool = False,
+        layer_id: int = 0,
+        rope_theta: float = 1000000,
+        max_position_embeddings: int = 32768,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        partial_rotary_factor: float = 1.0,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.layer_id = layer_id
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_heads
+        self.num_heads = self.total_num_heads // tp_size
+
+        self.total_num_kv_heads = num_kv_heads
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.head_dim = head_dim
+
+        self.v_head_dim = v_head_dim if v_head_dim is not None else head_dim
+
+        self.q_size = self.num_heads * self.head_dim
+        self.k_size = self.num_kv_heads * self.head_dim
+        self.v_size = self.num_kv_heads * self.v_head_dim
+
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+            v_head_size=self.v_head_dim,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.v_head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=True,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            max_position=max_position_embeddings,
+            rope_parameters={
+                "rope_type": "default",
+                "rope_theta": rope_theta,
+                "partial_rotary_factor": partial_rotary_factor,
+            },
+        )
+
+        self.attention_sink_bias = (
+            torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False)
+            if add_swa_attention_sink_bias
+            else None
+        )
+
+        sliding_window = sliding_window_size if sliding_window_size > -1 else None
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=sliding_window,
+            attn_type=AttentionType.DECODER,
+            prefix=f"{prefix}.attn",
+            sinks=self.attention_sink_bias,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+
+        v = v.view(-1, self.num_kv_heads, self.v_head_dim)
+        v = torch.nn.functional.pad(v, [0, self.head_dim - self.v_head_dim], value=0)
+        v = v.view(-1, self.num_kv_heads * self.head_dim)
+
+        attn_output = self.attn(q, k, v)
+
+        attn_output = attn_output.view(-1, self.num_heads, self.head_dim)[
+            ..., : self.v_head_dim
+        ].reshape(-1, self.num_heads * self.v_head_dim)
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MiMoV2FlashDecoderLayer(nn.Module):
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_text_config
+        quant_config = vllm_config.quant_config
+        layer_id = extract_layer_index(prefix)
+
+        self.hidden_size = config.hidden_size
+        self.config = config
+        self.layer_id = layer_id
+
+        rope_theta = getattr(config, "rope_theta", 1000000)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
+
+        if self.is_compressed_softmax_layer():
+            self.self_attn = MiMoV2Attention(
+                hidden_size=self.hidden_size,
+                num_heads=config.swa_num_attention_heads,
+                num_kv_heads=config.swa_num_key_value_heads,
+                head_dim=config.swa_head_dim,
+                v_head_dim=getattr(config, "swa_v_head_dim", None),
+                sliding_window_size=config.sliding_window_size,
+                attention_bias=config.attention_bias,
+                add_swa_attention_sink_bias=getattr(
+                    config, "add_swa_attention_sink_bias", False
+                ),
+                layer_id=layer_id,
+                rope_theta=getattr(config, "swa_rope_theta", rope_theta),
+                max_position_embeddings=max_position_embeddings,
+                quant_config=quant_config,
+                partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
+                prefix=f"{prefix}.self_attn",
+            )
+        else:
+            self.self_attn = MiMoV2Attention(
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                num_kv_heads=config.num_key_value_heads,
+                head_dim=config.head_dim,
+                v_head_dim=getattr(config, "v_head_dim", None),
+                sliding_window_size=-1,  # normal attention
+                attention_bias=config.attention_bias,
+                layer_id=layer_id,
+                rope_theta=rope_theta,
+                max_position_embeddings=max_position_embeddings,
+                quant_config=quant_config,
+                partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
+                prefix=f"{prefix}.self_attn",
+            )
+
+        self.is_layer_sparse = self.is_moe_layer(layer_id)
+        if self.is_layer_sparse:
+            self.mlp = MiMoV2MoE(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            self.mlp = MiMoV2MLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.layernorm_epsilon
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+    def is_moe_layer(self, layer_idx: int) -> bool:
+        return (
+            hasattr(self.config, "moe_layer_freq")
+            and layer_idx >= 0
+            and not isinstance(self.config.moe_layer_freq, int)
+            and self.config.moe_layer_freq[layer_idx]
+        )
+
+    def is_compressed_softmax_layer(self) -> bool:
+        return self.config.hybrid_layer_pattern[self.layer_id] == 1
+
+
+class MiMoV2Model(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config.get_text_config()
+        quant_config = vllm_config.quant_config
+        eplb_config = vllm_config.parallel_config.eplb_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.vocab_size = config.vocab_size
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+        self.v_scale = getattr(config, "attention_value_scale", None)
+
+        if get_pp_group().is_first_rank or (
+            config.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=f"{prefix}.embed_tokens",
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: MiMoV2FlashDecoderLayer(
+                vllm_config=vllm_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        else:
+            self.norm = PPMissingLayer()
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer)
+        ):
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return 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.n_routed_experts,
+            num_redundant_experts=self.num_redundant_experts,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        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),
+        ]
+
+        tp_rank = get_tensor_model_parallel_rank()
+        tp_size = get_tensor_model_parallel_world_size()
+
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                continue
+            if "mtp" in name:
+                continue
+
+            if self.quant_config is not None:
+                cache_scale_name = self.quant_config.get_cache_scale(name)
+                if cache_scale_name is not None and cache_scale_name in params_dict:
+                    param = params_dict[cache_scale_name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+
+                    kv_scale = loaded_weight
+                    if kv_scale.dim() > 0 and kv_scale.numel() > 1:
+                        kv_scale = kv_scale.view(-1)[0]
+
+                    weight_loader(param, kv_scale)
+                    loaded_params.add(cache_scale_name)
+                    continue
+
+            expert_matched = False
+            for param_name, weight_name, expert_id, shard_id in expert_params_mapping:
+                if weight_name not in name:
+                    continue
+
+                name_rewritten = name.replace(weight_name, param_name)
+
+                if is_pp_missing_parameter(name_rewritten, self):
+                    continue
+
+                if (
+                    name_rewritten.endswith(".bias") or name_rewritten.endswith("_bias")
+                ) and name_rewritten not in params_dict:
+                    continue
+
+                if name_rewritten not in params_dict:
+                    continue
+
+                param = params_dict[name_rewritten]
+                weight_loader = param.weight_loader
+
+                weight_loader(
+                    param,
+                    loaded_weight,
+                    name_rewritten,
+                    shard_id=shard_id,
+                    expert_id=expert_id,
+                )
+                loaded_params.add(name_rewritten)
+                expert_matched = True
+                break
+
+            if expert_matched:
+                continue
+
+            stacked_matched = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name_rewritten = name.replace(weight_name, param_name)
+
+                if (
+                    name_rewritten.endswith(".bias")
+                    and name_rewritten not in params_dict
+                ):
+                    continue
+
+                if is_pp_missing_parameter(name_rewritten, self):
+                    continue
+
+                if name_rewritten not in params_dict:
+                    continue
+
+                param = params_dict[name_rewritten]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+
+                if param_name == "qkv_proj" and shard_id == "v":
+                    v_scale = (
+                        self.v_scale
+                        if self.v_scale is not None
+                        else getattr(self.config, "attention_value_scale", None)
+                    )
+                    if v_scale is not None and (
+                        name.endswith("weight_scale_inv") or name.endswith(".bias")
+                    ):
+                        loaded_weight *= float(v_scale)
+
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name_rewritten)
+
+                stacked_matched = True
+                break
+
+            if stacked_matched:
+                continue
+
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+
+            orig_name = name
+            mapped_name = maybe_remap_kv_scale_name(name, params_dict)
+            name = mapped_name if mapped_name is not None else orig_name
+
+            if name not in params_dict:
+                continue
+
+            param = params_dict[name]
+
+            if "attention_sink_bias" in name:
+                total_heads = loaded_weight.shape[0]
+                heads_per_rank = total_heads // tp_size
+                head_start = tp_rank * heads_per_rank
+                narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
+
+                param.data.copy_(narrow_weight)
+                loaded_params.add(name)
+            else:
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded_params.add(name)
+
+        return loaded_params
+
+
+class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.model = MiMoV2Model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        self.model.aux_hidden_state_layers = layers
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        num_layers = len(self.model.layers)
+        return (2, num_layers // 2, num_layers - 3)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -152,6 +152,7 @@ _TEXT_GENERATION_MODELS = {
     "MptForCausalLM": ("mpt", "MPTForCausalLM"),
     "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
     "MiMoForCausalLM": ("mimo", "MiMoForCausalLM"),
+    "MiMoV2FlashForCausalLM": ("mimo_v2_flash", "MiMoV2FlashForCausalLM"),
     "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
     "NemotronHForCausalLM": ("nemotron_h", "NemotronHForCausalLM"),
     "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),