[Model] Add LongCat-Flash (#23991)

Signed-off-by: yangxurui <yangxurui@meituan.com>
Co-authored-by: yangxurui <yangxurui@meituan.com>

vllm/model_executor/layers/quantization/mxfp4.py

@@ -718,7 +718,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
 
         assert isinstance(self.fused_experts, mk.FusedMoEModularKernel)
 
-        topk_weights, topk_ids = FusedMoE.select_experts(
+        topk_weights, topk_ids, _ = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
             use_grouped_topk=use_grouped_topk,
@@ -783,7 +783,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             raise NotImplementedError("EPLB is not supported for mxfp4")
 
         if self.mxfp4_backend == Mxfp4Backend.MARLIN:
-            topk_weights, topk_ids = FusedMoE.select_experts(
+            topk_weights, topk_ids, _ = FusedMoE.select_experts(
                 hidden_states=x,
                 router_logits=router_logits,
                 use_grouped_topk=use_grouped_topk,
@@ -894,7 +894,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
               or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16):
             from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe
 
-            topk_weights, topk_ids = FusedMoE.select_experts(
+            topk_weights, topk_ids, _ = FusedMoE.select_experts(
                 hidden_states=x,
                 router_logits=router_logits,
                 use_grouped_topk=use_grouped_topk,

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -329,7 +329,7 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
             raise NotImplementedError(
                 "EPLB not supported for `QuarkW8A8Fp8MoEMethod` yet.")
 
-        topk_weights, topk_ids = FusedMoE.select_experts(
+        topk_weights, topk_ids, _ = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
             use_grouped_topk=use_grouped_topk,
@@ -531,7 +531,7 @@ class QuarkW4A4MXFp4MoEMethod(QuarkMoEMethod):
 
         from vllm.model_executor.layers.fused_moe import fused_experts
 
-        topk_weights, topk_ids = FusedMoE.select_experts(
+        topk_weights, topk_ids, _ = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
             use_grouped_topk=use_grouped_topk,

vllm/model_executor/layers/quantization/rtn.py

@@ -318,7 +318,7 @@ class RTNMoEMethod(FusedMoEMethodBase):
 
         from vllm.model_executor.layers.fused_moe import fused_experts
 
-        topk_weights, topk_ids = FusedMoE.select_experts(
+        topk_weights, topk_ids, _ = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
             use_grouped_topk=use_grouped_topk,

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

@@ -292,6 +292,11 @@ def is_layer_skipped(
                     f"Detected some but not all shards of {prefix} "
                     "are quantized. All shards of fused layers "
                     "to have the same precision.")
+    elif "experts" in prefix:
+        return any([
+            prefix in layer_name for layer_name in ignored_layers
+            if "experts" in layer_name
+        ])
     else:
         is_skipped = prefix in ignored_layers
 

vllm/model_executor/models/longcat_flash_mtp.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/deepseek_mtp.py
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.utils.int8_utils import (
+    block_dequant)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.longcat_flash import FlashConfig
+from vllm.sequence import IntermediateTensors
+
+from .deepseek_v2 import DeepseekV2DecoderLayer
+from .interfaces import SupportsPP
+from .utils import maybe_prefix
+
+
+class LongCatMultiTokenPredictorLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        vllm_config: VllmConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        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 = ReplicatedLinear(2 * config.hidden_size,
+                                        config.hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config,
+                                        prefix="eh_proj")
+        self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix)
+        self.final_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        inputs_embeds = self.enorm(inputs_embeds)
+        previous_hidden_states = self.hnorm(previous_hidden_states)
+
+        hidden_states, _ = self.eh_proj(
+            torch.cat([inputs_embeds, previous_hidden_states], dim=-1))
+
+        hidden_states, residual = self.mtp_block(positions=positions,
+                                                 hidden_states=hidden_states,
+                                                 residual=None)
+        hidden_states, _ = self.final_layernorm(hidden_states, residual)
+        return hidden_states
+
+
+class LongCatMultiTokenPredictor(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
+        vllm_config.model_config.hf_config.intermediate_size \
+                                                = config.intermediate_size
+        self.mtp_start_layer_idx = config.num_hidden_layers * 2
+        self.num_mtp_layers = 1
+        self.layers = torch.nn.ModuleDict({
+            str(idx):
+            LongCatMultiTokenPredictorLayer(
+                config,
+                prefix=f"{prefix}.layers.{idx}",
+                vllm_config=vllm_config,
+                quant_config=quant_config,
+            )
+            for idx in range(self.mtp_start_layer_idx,
+                             self.mtp_start_layer_idx + self.num_mtp_layers)
+        })
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        current_step_idx = (spec_step_idx % self.num_mtp_layers)
+        return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
+            input_ids,
+            positions,
+            previous_hidden_states,
+            inputs_embeds,
+            current_step_idx,
+        )
+
+
+class LongCatFlashMTP(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        # LongCat MTP without MoE layers
+        vllm_config.model_config.hf_config.n_routed_experts = None
+        self.config = FlashConfig(
+            **vllm_config.model_config.hf_config.__dict__)
+        self.quant_config = None if "mtp" in getattr(
+            self.config, "disable_quant_module",
+            []) else vllm_config.quant_config
+
+        self.model = LongCatMultiTokenPredictor(vllm_config=vllm_config,
+                                                quant_config=self.quant_config,
+                                                prefix=maybe_prefix(
+                                                    prefix, "model"))
+        self.lm_head = ParallelLMHead(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            quant_config=self.quant_config,
+        )
+        self.logits_processor = LogitsProcessor(self.config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, hidden_states,
+                                   inputs_embeds, spec_step_idx)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> Optional[torch.Tensor]:
+
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+            ("fused_qkv_a_proj", "q_a_proj", 0),
+            ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
+        ]
+
+        new_to_old_names_mapping = {
+            "model.mtp.embed_tokens.weight":
+            "model.layers.0.embed_tokens.weight",
+            "model.mtp.layers.0.eh_proj.weight": "eh_proj.weight",
+            "model.mtp.layers.0.eh_proj.weight_scale_inv":
+            "eh_proj.weight_scale_inv",
+            "model.mtp.layers.0.enorm.m.weight": "enorm.weight",
+            "model.mtp.layers.0.hnorm.m.weight": "hnorm.weight",
+            "model.mtp.layers.0.input_layernorm.weight":
+            "model.layers.0.input_layernorm.weight",
+            "model.mtp.layers.0.post_attention_layernorm.weight":
+            "model.layers.0.post_attention_layernorm.weight",
+            "model.mtp.layers.0.self_attn.kv_a_layernorm.weight":
+            "model.layers.0.self_attn.kv_a_layernorm.weight",
+            "model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight":
+            "model.layers.0.self_attn.kv_a_proj_with_mqa.weight",
+            "model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv":
+            "model.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.kv_b_proj.weight":
+            "model.layers.0.self_attn.kv_b_proj.weight",
+            "model.mtp.layers.0.self_attn.kv_b_proj.weight_scale_inv":
+            "model.layers.0.self_attn.kv_b_proj.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.o_proj.weight":
+            "model.layers.0.self_attn.o_proj.weight",
+            "model.mtp.layers.0.self_attn.o_proj.weight_scale_inv":
+            "model.layers.0.self_attn.o_proj.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.q_a_layernorm.weight":
+            "model.layers.0.self_attn.q_a_layernorm.weight",
+            "model.mtp.layers.0.self_attn.q_a_proj.weight":
+            "model.layers.0.self_attn.q_a_proj.weight",
+            "model.mtp.layers.0.self_attn.q_a_proj.weight_scale_inv":
+            "model.layers.0.self_attn.q_a_proj.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.q_b_proj.weight":
+            "model.layers.0.self_attn.q_b_proj.weight",
+            "model.mtp.layers.0.self_attn.q_b_proj.weight_scale_inv":
+            "model.layers.0.self_attn.q_b_proj.weight_scale_inv",
+            "model.mtp.layers.0.transformer_layer.mlp.down_proj.weight":
+            "model.layers.0.mlp.down_proj.weight",
+            "model.mtp.layers.0.transformer_layer.mlp.down_proj.weight_scale_inv":
+            "model.layers.0.mlp.down_proj.weight_scale_inv",
+            "model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight":
+            "model.layers.0.mlp.gate_proj.weight",
+            "model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight_scale_inv":
+            "model.layers.0.mlp.gate_proj.weight_scale_inv",
+            "model.mtp.layers.0.transformer_layer.mlp.up_proj.weight":
+            "model.layers.0.mlp.up_proj.weight",
+            "model.mtp.layers.0.transformer_layer.mlp.up_proj.weight_scale_inv":
+            "model.layers.0.mlp.up_proj.weight_scale_inv",
+            "model.mtp.norm.weight": "final_layernorm.weight",
+        }
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            spec_layer = self.get_spec_layer_idx_from_weight_name(
+                self.config, name)
+            if spec_layer is None:
+                continue
+            name = self._rewrite_spec_layer_name(spec_layer, name,
+                                                 new_to_old_names_mapping)
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if (("mlp.experts." in name) and name not in params_dict):
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                # QKV fusion is optional, fall back to normal
+                # weight loading if it's not enabled
+                if ((param_name == "fused_qkv_a_proj")
+                        and name not in params_dict):
+                    continue
+
+                # 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
+
+                # According to DeepSeek-V3 Technical Report, MTP modules
+                # shares embedding layer. We only load the first weights.
+                if (spec_layer != self.model.mtp_start_layer_idx
+                        and ".layers" not in name):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        spec_layer_id = self.config.num_hidden_layers * 2
+        self_attn = self.model.layers[str(spec_layer_id)].mtp_block.self_attn
+        if hasattr(
+                self.quant_config,
+                "weight_block_size") and self_attn.kv_b_proj.weight.dtype in (
+                    torch.float8_e4m3fn,
+                    torch.float8_e4m3fnuz,
+                ):
+            weight_block_size = self.quant_config.weight_block_size
+            if weight_block_size is not None:
+                dtype = torch.get_default_dtype()
+                w = block_dequant(self_attn.kv_b_proj.weight,
+                                  self_attn.kv_b_proj.weight_scale_inv,
+                                  weight_block_size).to(dtype)
+            else:
+                w = self_attn.kv_b_proj.weight
+        else:
+            w = self_attn.kv_b_proj.weight
+        w_kc, w_vc = w.unflatten(
+            0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)).split(
+                [self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
+        self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
+        self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
+        if self.config.mla_scale_q_lora:
+            self_attn.q_a_layernorm.weight.data *= (
+                self.config.hidden_size / self.config.q_lora_rank)**0.5
+        if self.config.mla_scale_kv_lora:
+            self_attn.kv_a_layernorm.weight.data *= (
+                self.config.hidden_size / self.config.kv_lora_rank)**0.5
+        return loaded_params
+
+    def _rewrite_spec_layer_name(self, spec_layer: int, name: str,
+                                 new_to_old_names_mapping: dict) -> str:
+        """
+        Rewrite the weight name to match the format of the original model.
+        Add .mtp_block for modules in transformer layer block for spec layer
+        and rename shared layer weights to be top level.
+        """
+        if name in new_to_old_names_mapping:
+            name = new_to_old_names_mapping[name]
+        spec_layer_weight_names = [
+            "embed_tokens", "enorm", "hnorm", "eh_proj", "shared_head"
+        ]
+        if name.startswith("enorm") or name.startswith(
+                "hnorm") or name.startswith("eh_proj") or name.startswith(
+                    "final_layernorm"):
+            name = "model.layers." + str(spec_layer) + "." + name
+        shared_weight_names = ["embed_tokens"]
+        spec_layer_weight = False
+        shared_weight = False
+        for weight_name in spec_layer_weight_names:
+            if weight_name in name:
+                spec_layer_weight = True
+                if weight_name in shared_weight_names:
+                    shared_weight = True
+                break
+        if not spec_layer_weight:
+            # treat rest weights as weights for transformer layer block
+            name = name.replace("model.layers.0.",
+                                f"model.layers.{spec_layer}.mtp_block.")
+        elif shared_weight:
+            # treat shared weights as top level weights
+            name = name.replace("model.layers.0.", "model.")
+        return name
+
+    def get_spec_layer_idx_from_weight_name(self, config: PretrainedConfig,
+                                            weight_name: str) -> Optional[int]:
+        if "model.mtp" in weight_name:
+            return config.num_hidden_layers * 2
+        return None

vllm/model_executor/models/registry.py

@@ -109,6 +109,7 @@ _TEXT_GENERATION_MODELS = {
     "Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"),  # noqa: E501
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
+    "LongcatFlashForCausalLM": ("longcat_flash", "LongcatFlashForCausalLM"),
     "MambaForCausalLM": ("mamba", "MambaForCausalLM"),
     "FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
     "FalconH1ForCausalLM":("falcon_h1", "FalconH1ForCausalLM"),
@@ -287,6 +288,7 @@ _SPECULATIVE_DECODING_MODELS = {
     "EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"),
     "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
     "ErnieMTPModel": ("ernie_mtp", "ErnieMTP"),
+    "LongCatFlashMTPModel": ("longcat_flash_mtp", "LongCatFlashMTP"),
     "Glm4MoeMTPModel": ("glm4_moe_mtp", "Glm4MoeMTP"),
     "MedusaModel": ("medusa", "Medusa"),
     "Qwen3NextMTP": ("qwen3_next_mtp", "Qwen3NextMTP"),

vllm/model_executor/models/utils.py

@@ -691,14 +691,14 @@ def maybe_prefix(prefix: str, name: str) -> str:
     return name if not prefix else f"{prefix}.{name}"
 
 
-def extract_layer_index(layer_name: str) -> int:
+def extract_layer_index(layer_name: str, num_attn_module: int = 1) -> int:
     """
     Extract the layer index from the module name.
     Examples:
     - "encoder.layers.0" -> 0
     - "encoder.layers.1.self_attn" -> 1
     - "2.self_attn" -> 2
-    - "model.encoder.layers.0.sub.1" -> ValueError
+    - "model.encoder.layers.0.sub.1" -> ValueError if num_attn_module == 1
     """
     subnames = layer_name.split(".")
     int_vals: list[int] = []
@@ -707,9 +707,17 @@ def extract_layer_index(layer_name: str) -> int:
             int_vals.append(int(subname))
         except ValueError:
             continue
+    if num_attn_module == 1 or "attn" not in layer_name:
         assert len(int_vals) == 1, (f"layer name {layer_name} should"
                                     " only contain one integer")
+
         return int_vals[0]
+    else:
+        assert len(int_vals) <= 2, (f"layer name {layer_name} should"
+                                    " contain most two integers")
+        layer_index = int_vals[0] * num_attn_module + int_vals[1] if len(
+            int_vals) == 2 else int_vals[0]
+        return layer_index
 
 
 def cast_overflow_tensors(

vllm/v1/spec_decode/eagle.py

@@ -169,7 +169,6 @@ class EagleProposer:
             target_hidden_states = self.model.combine_hidden_states(
                 target_hidden_states)
             assert target_hidden_states.shape[-1] == self.hidden_size
-
         # Shift the input ids by one token.
         # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
         self.input_ids[:num_tokens - 1] = target_token_ids[1:]
@@ -223,7 +222,8 @@ class EagleProposer:
                 hidden_states=self.hidden_states[:num_input_tokens],
                 inputs_embeds=inputs_embeds,
             )
-            if self.method in ("deepseek_mtp", "ernie_mtp", "qwen3_next_mtp"):
+            if self.method in ("deepseek_mtp", "ernie_mtp", "qwen3_next_mtp",
+                               "longcat_flash_mtp"):
                 last_hidden_states = ret_hidden_states
                 hidden_states = last_hidden_states
             else:
@@ -237,6 +237,9 @@ class EagleProposer:
             return draft_token_ids.view(-1, 1)
 
         positions = target_positions[last_token_indices]
+        if self.method in ("deepseek_mtp", "ernie_mtp", "longcat_flash_mtp"):
+            hidden_states = self.hidden_states[last_token_indices]
+        else:
             hidden_states = hidden_states[last_token_indices]
 
         if isinstance(attn_metadata, TreeAttentionMetadata):
@@ -350,7 +353,7 @@ class EagleProposer:
                     inputs_embeds=inputs_embeds,
                 )
                 if self.method in ("deepseek_mtp", "ernie_mtp",
-                                   "qwen3_next_mtp"):
+                                   "qwen3_next_mtp", "longcat_flash_mtp"):
                     last_hidden_states = ret_hidden_states
                     hidden_states = ret_hidden_states
                 else:

vllm/v1/worker/gpu_model_runner.py

@@ -3840,9 +3840,11 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                          target_layer_name)
             kv_caches[layer_name] = kv_caches[target_layer_name]
 
+        num_attn_module = 2 \
+            if self.model_config.hf_config.model_type == "longcat_flash" else 1
         bind_kv_cache(kv_caches,
                       self.compilation_config.static_forward_context,
-                      self.kv_caches)
+                      self.kv_caches, num_attn_module)
         return kv_caches
 
     def maybe_add_kv_sharing_layers_to_kv_cache_groups(

vllm/v1/worker/utils.py

@@ -266,6 +266,7 @@ def bind_kv_cache(
     kv_caches: dict[str, torch.Tensor],
     forward_context: dict[str, "Attention"],
     runner_kv_caches: list[torch.Tensor],
+    num_attn_module: Optional[int] = 1,
 ) -> None:
     """
     Bind the allocated KV cache to both ModelRunner and forward context so
@@ -289,7 +290,8 @@ def bind_kv_cache(
     # Convert kv_caches dict to a list of tensors in the order of layer_index.
     index2name = defaultdict(list)
     for layer_name in kv_caches:
-        index2name[extract_layer_index(layer_name)].append(layer_name)
+        index2name[extract_layer_index(layer_name,
+                                       num_attn_module)].append(layer_name)
 
     for layer_index in sorted(index2name.keys()):
         layer_names = index2name[layer_index]