[Model] Add support for BharatGen's Param2MoE model (#38000)

Signed-off-by: bhargav-patel-29 <bhargav.patel@tihiitb.org>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

docs/models/supported_models.md

@@ -457,6 +457,7 @@ th {
 | `PanguEmbeddedForCausalLM` | openPangu-Embedded-7B | `FreedomIntelligence/openPangu-Embedded-7B-V1.1` | ✅︎ | ✅︎ |
 | `PanguProMoEV2ForCausalLM` | openpangu-pro-moe-v2 | | ✅︎ | ✅︎ |
 | `PanguUltraMoEForCausalLM` | openpangu-ultra-moe-718b-model | `FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1` | ✅︎ | ✅︎ |
+| `Param2MoEForCausalLM` | param2moe | `bharatgenai/Param2-17B-A2.4B-Thinking`, etc. | ✅︎ | ✅︎ |
 | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ |
 | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ |
 | `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -461,6 +461,10 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
         trust_remote_code=True,
         is_available_online=False,
     ),
+    "Param2MoEForCausalLM": _HfExamplesInfo(
+        "bharatgenai/Param2-17B-A2.4B-Thinking",
+        trust_remote_code=True,
+    ),
     "PersimmonForCausalLM": _HfExamplesInfo("adept/persimmon-8b-chat"),
     "PhiForCausalLM": _HfExamplesInfo("microsoft/phi-2"),
     "Phi3ForCausalLM": _HfExamplesInfo("microsoft/Phi-3-mini-4k-instruct"),

vllm/model_executor/models/param2moe.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Copyright 2026 BharatGen AI team. All rights reserved.
+#
+# This code has been modified to accommodate Param2MoE's GQA-based MoE architecture.
+#
+# 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.
+# limitations under the License.
+from __future__ import annotations
+
+from collections.abc import Iterable, Iterator
+from itertools import islice
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+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
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+
+def _is_expert_bias_name(name: str) -> bool:
+    """True when the weight is the MoE router's per-expert score bias."""
+    return name.endswith(".mlp.gate.expert_bias")
+
+
+def _zero_mean_tensor(t: torch.Tensor) -> torch.Tensor:
+    if t.numel() == 0:
+        return t
+    return t - t.mean()
+
+
+def _rename_and_normalize_weights(
+    weights: Iterable[tuple[str, torch.Tensor]],
+) -> Iterator[tuple[str, torch.Tensor]]:
+    """
+    Translate HuggingFace Param2MoE weight names to vLLM internal names
+    and zero-mean the expert-bias tensor so the router stays balanced.
+
+    Mapping table (HF → vLLM):
+      model.word_embeddings.*              → model.embed_tokens.*
+      *.attention.query_key_value.*        → *.self_attn.qkv_proj.*
+      *.attention.dense.*                  → *.self_attn.o_proj.*
+      *.attention.query_layernorm.*        → *.self_attn.q_layernorm.*
+      *.attention.key_layernorm.*          → *.self_attn.k_layernorm.*
+      *.mlp.gate.expert_bias               → *.mlp.gate.e_score_correction_bias
+        (also zero-meant for load balance)
+    """
+    for name, w in weights:
+        # Embedding table
+        name = name.replace("model.word_embeddings.", "model.embed_tokens.")
+        # Fused QKV projection  (HF: query_key_value → vLLM: qkv_proj)
+        name = name.replace(".attention.query_key_value.", ".self_attn.qkv_proj.")
+        # Output projection  (HF: dense → vLLM: o_proj)
+        name = name.replace(".attention.dense.", ".self_attn.o_proj.")
+        # Per-head query norm
+        name = name.replace(".attention.query_layernorm.", ".self_attn.q_layernorm.")
+        # Per-head key norm
+        name = name.replace(".attention.key_layernorm.", ".self_attn.k_layernorm.")
+        # Catch any remaining .attention. → .self_attn. prefixes
+        # (e.g. future bias params on the projection layers)
+        name = name.replace(".attention.", ".self_attn.")
+
+        # Expert-score bias: rename + zero-mean
+        if name.endswith(".mlp.gate.expert_bias"):
+            name = name.replace(
+                ".mlp.gate.expert_bias",
+                ".mlp.gate.e_score_correction_bias",
+            )
+            w = _zero_mean_tensor(w)
+
+        yield name, w
+
+
+class Param2MoEAttention(nn.Module):
+    """
+    Grouped-Query Attention (GQA) for Param2MoE.
+
+    Notable differences from a vanilla GQA layer:
+      * The checkpoint fuses Q, K, V into a single ``query_key_value`` weight.
+        vLLM receives it already renamed to ``qkv_proj`` by the weight-name
+        translator and splits it during ``load_weights``.
+      * Optional per-head RMS norms on Q and K (``use_qk_norm=True``).
+    """
+
+    def __init__(
+        self,
+        config,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim or (self.hidden_size // self.num_heads)
+        self.use_qk_norm: bool = getattr(config, "use_qk_norm", False)
+
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.num_heads % tp_size == 0, (
+            f"num_attention_heads ({self.num_heads}) must be divisible "
+            f"by tensor-parallel world size ({tp_size})."
+        )
+        assert self.num_kv_heads % tp_size == 0, (
+            f"num_key_value_heads ({self.num_kv_heads}) must be divisible "
+            f"by tensor-parallel world size ({tp_size})."
+        )
+        self.num_local_heads = self.num_heads // tp_size
+        self.num_local_kv_heads = self.num_kv_heads // tp_size
+
+        # Sizes after TP split (used in forward to split qkv output)
+        self.q_size_local = self.num_local_heads * self.head_dim
+        self.kv_size_local = self.num_local_kv_heads * self.head_dim
+
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.num_heads,
+            total_num_kv_heads=self.num_kv_heads,
+            bias=getattr(config, "use_qkv_bias", False),
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.num_heads * self.head_dim,
+            output_size=self.hidden_size,
+            bias=getattr(config, "use_bias", False),
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        if self.use_qk_norm:
+            self.q_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+        # `partial_rotary_factor` defaults to 1.0 (full RoPE) if not in config
+        partial_rotary_factor: float = getattr(config, "partial_rotary_factor", 1.0)
+        rope_dim = int(self.head_dim * partial_rotary_factor)
+
+        rope_parameters: dict = {
+            "rope_type": "default",
+            "base": config.rope_theta,
+        }
+        if config.rope_scaling is not None:
+            rope_parameters.update(config.rope_scaling)
+            # Normalise key: some checkpoints use "type", vLLM wants "rope_type"
+            if "type" in rope_parameters and "rope_type" not in rope_parameters:
+                rope_parameters["rope_type"] = rope_parameters.pop("type")
+
+        self.rotary_emb = get_rope(
+            rope_dim,
+            max_position=config.max_position_embeddings,
+            rope_parameters=rope_parameters,
+            is_neox_style=True,
+        )
+
+        self.attn = Attention(
+            num_heads=self.num_heads,
+            head_size=self.head_dim,
+            scale=self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # 1. Fused QKV projection → split into local Q / K / V
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split(
+            [self.q_size_local, self.kv_size_local, self.kv_size_local],
+            dim=-1,
+        )
+
+        # 2. Optional per-head QK norms
+        #    Reshape to (T, num_local_heads, head_dim), norm, reshape back.
+        if self.use_qk_norm:
+            T = q.shape[0]
+            q = self.q_layernorm(q.view(T, self.num_local_heads, self.head_dim)).view(
+                T, self.q_size_local
+            )
+            k = self.k_layernorm(
+                k.view(T, self.num_local_kv_heads, self.head_dim)
+            ).view(T, self.kv_size_local)
+
+        # 3. Rotary position embeddings
+        q, k = self.rotary_emb(positions, q, k)
+
+        # 4. Paged attention
+        attn_output = self.attn(q, k, v)
+
+        # 5. Output projection
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Param2MoEMLP(nn.Module):
+    """SwiGLU feed-forward block used for dense layers."""
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=config.hidden_size,
+            output_sizes=[intermediate_size, intermediate_size],
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Param2MoEMoEBlock(nn.Module):
+    """
+    Mixture-of-Experts block for Param2MoE.
+
+    Routing:
+      * Sigmoid scoring  (config.score_function = "sigmoid")
+      * Grouped top-k   (n_group, topk_group)
+      * Per-expert bias  (gate.expert_bias → e_score_correction_bias)
+      * routed_scaling_factor normalisation
+
+    One set of shared (always-active) experts is added on top.
+    """
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.hidden_size = config.hidden_size
+
+        self.num_experts: int = config.num_experts
+        self.top_k: int = config.num_experts_per_tok
+        self.routed_scaling_factor: float = getattr(
+            config, "routed_scaling_factor", 1.0
+        )
+
+        self.n_group: int | None = getattr(config, "n_group", None)
+        self.topk_group: int | None = getattr(config, "topk_group", None)
+        self.use_grouped_topk: bool = (
+            self.n_group is not None and self.topk_group is not None
+        )
+
+        self.norm_expert_prob: bool = getattr(config, "norm_topk_prob", True)
+        self.score_function: str = getattr(config, "score_function", "sigmoid")
+
+        self.gate = nn.Linear(
+            self.hidden_size,
+            self.num_experts,
+            bias=False,
+        )
+
+        if getattr(config, "moe_router_enable_expert_bias", True):
+            self.gate.e_score_correction_bias = nn.Parameter(
+                torch.zeros(self.num_experts, dtype=torch.float32)
+            )
+        else:
+            self.gate.e_score_correction_bias = None  # type: ignore[assignment]
+
+        self.num_shared_experts: int = getattr(config, "num_shared_experts", 1)
+        if self.num_shared_experts > 0:
+            # If moe_shared_expert_intermediate_size is present in the config
+            # it already encodes the TOTAL intermediate size across all shared
+            # experts (i.e. it equals moe_intermediate_size * num_shared_experts).
+            # Do NOT multiply again.  Fall back to computing the product only
+            # when the dedicated field is absent.
+            if (
+                hasattr(config, "moe_shared_expert_intermediate_size")
+                and config.moe_shared_expert_intermediate_size is not None
+            ):
+                shared_int: int = config.moe_shared_expert_intermediate_size
+            else:
+                shared_int = config.moe_intermediate_size * self.num_shared_experts
+            self.shared_experts = Param2MoEMLP(
+                intermediate_size=shared_int,
+                config=config,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=f"{prefix}.shared_experts",
+            )
+        else:
+            self.shared_experts = None  # type: ignore[assignment]
+
+        self.experts = SharedFusedMoE(
+            shared_experts=self.shared_experts,
+            num_experts=self.num_experts,
+            top_k=self.top_k,
+            hidden_size=self.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=self.norm_expert_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+            scoring_func=self.score_function,
+            e_score_correction_bias=self.gate.e_score_correction_bias,
+            num_expert_group=self.n_group,
+            topk_group=self.topk_group,
+            use_grouped_topk=self.use_grouped_topk,
+            routed_scaling_factor=self.routed_scaling_factor,
+        )
+
+    def maybe_get_fused_moe(self) -> SharedFusedMoE:
+        return self.experts
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        # Router: both input and weight must be float32 for numerical
+        # stability (mirrors the original Param2MoEGate behaviour).
+        # The gate nn.Linear weight lives in the model dtype (bfloat16),
+        # so we must cast both explicitly via F.linear instead of calling
+        # self.gate() which would hit a dtype mismatch.
+        router_logits = F.linear(
+            hidden_states.float(),
+            self.gate.weight.float(),
+        ).to(hidden_states.dtype)
+
+        final_hidden = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+
+        if self.shared_experts is not None:
+            shared_output, expert_output = final_hidden
+        else:
+            shared_output, expert_output = None, final_hidden
+
+        if shared_output is not None:
+            expert_output = expert_output + shared_output
+
+        if self.tp_size > 1:
+            expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
+                expert_output
+            )
+
+        return expert_output.view(num_tokens, hidden_dim)
+
+
+class Param2MoEDecoderLayer(nn.Module):
+    """
+    Single transformer decoder block.
+
+    Dense for the first ``first_k_dense_replace`` layers; MoE thereafter.
+    """
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        hidden_size = config.hidden_size
+        # Derive the layer index from the prefix (e.g. "model.layers.3")
+        layer_idx = int(prefix.split(".")[-1])
+
+        self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = Param2MoEAttention(
+            config=config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.post_attention_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
+
+        first_k_dense: int = getattr(config, "first_k_dense_replace", 1)
+        is_moe_layer = config.num_experts is not None and layer_idx >= first_k_dense
+
+        if is_moe_layer:
+            self.mlp = Param2MoEMoEBlock(
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            self.mlp = Param2MoEMLP(  # type: ignore[assignment]
+                intermediate_size=config.intermediate_size,
+                config=config,
+                quant_config=quant_config,
+                reduce_results=True,
+                prefix=f"{prefix}.mlp",
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        positions: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Pre-norm + attention
+        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,
+        )
+
+        # Pre-norm + MLP
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class Param2MoEModel(nn.Module):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.embed_dim = config.hidden_size
+        self.tie_word_embeddings: bool = getattr(config, "tie_word_embeddings", False)
+
+        # Embedding  (HF name: word_embeddings → vLLM name: embed_tokens)
+        if get_pp_group().is_first_rank or (
+            self.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                self.embed_dim,
+                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: Param2MoEDecoderLayer(
+                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(self.embed_dim, eps=config.rms_norm_eps)
+        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,
+        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 layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states, residual = layer(hidden_states, positions, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        if residual is None:
+            hidden_states = self.norm(hidden_states)
+        else:
+            hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        """
+        Custom weight loader for the inner Param2MoEModel.
+
+        Receives weights that have already been renamed/normalised by the
+        outer model and whose ``model.`` prefix has been stripped by
+        ``AutoWeightsLoader``.  Handles:
+          1. Fused QKV split (query_key_value → qkv_proj q/k/v shards).
+          2. gate_proj + up_proj → gate_up_proj stacking (dense + shared-exp).
+          3. Routed-expert weights via the fused-MoE mapping.
+          4. All remaining weights via their default loader.
+        """
+        config = self.config
+        num_heads: int = config.num_attention_heads
+        num_kv_heads: int = config.num_key_value_heads
+        head_dim: int = config.head_dim or (config.hidden_size // num_heads)
+        q_split = num_heads * head_dim
+        kv_split = num_kv_heads * head_dim
+
+        stacked_params_mapping = [
+            # (vllm_param_name, ckpt_weight_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        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:
+            # ------------------------------------------------------------------
+            # 1. Fused QKV: split into q / k / v shards for QKVParallelLinear
+            # ------------------------------------------------------------------
+            if name.endswith(".self_attn.qkv_proj.weight"):
+                if name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                q_w = loaded_weight[:q_split, :]
+                k_w = loaded_weight[q_split : q_split + kv_split, :]
+                v_w = loaded_weight[q_split + kv_split :, :]
+                weight_loader(param, q_w, "q")
+                weight_loader(param, k_w, "k")
+                weight_loader(param, v_w, "v")
+                loaded_params.add(name)
+                continue
+
+            # ------------------------------------------------------------------
+            # 2. gate_proj / up_proj → gate_up_proj (dense MLP + shared-exp.)
+            # ------------------------------------------------------------------
+            matched_stacked = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:  # routed experts handled below
+                    continue
+                new_name = name.replace(weight_name, param_name)
+                if new_name.endswith(".bias") and new_name not in params_dict:
+                    continue
+                if new_name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(new_name, self):
+                    continue
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(new_name)
+                matched_stacked = True
+                break
+
+            if matched_stacked:
+                continue
+
+            # ------------------------------------------------------------------
+            # 3. Routed expert weights → fused-MoE kernel layout
+            # ------------------------------------------------------------------
+            matched_expert = False
+            for (
+                param_name,
+                weight_name,
+                expert_id,
+                shard_id,
+            ) in expert_params_mapping:
+                if weight_name not in name:
+                    continue
+                new_name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(new_name, self):
+                    continue
+                if new_name not in params_dict:
+                    continue
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(
+                    param,
+                    loaded_weight,
+                    name,
+                    shard_id=shard_id,
+                    expert_id=expert_id,
+                )
+                loaded_params.add(new_name)
+                matched_expert = True
+                break
+
+            if matched_expert:
+                continue
+
+            # ------------------------------------------------------------------
+            # 4. All other weights: direct load (layernorms, embed_tokens, …)
+            # ------------------------------------------------------------------
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if name not in params_dict:
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            try:
+                weight_loader(param, loaded_weight)
+            except Exception as e:
+                raise RuntimeError(
+                    f"[param2moe] Failed to load weight '{name}' "
+                    f"with shape {tuple(loaded_weight.shape)} "
+                    f"into param type {type(param).__name__}: {e}"
+                ) from e
+            loaded_params.add(name)
+
+        return loaded_params
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return SharedFusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
+
+class Param2MoEMixtureOfExperts(MixtureOfExperts):
+    """Implements the vLLM MixtureOfExperts protocol for Param2MoE."""
+
+    expert_weights: list[torch.Tensor]
+
+    def extract_moe_parameters(self, example_moe: Param2MoEMoEBlock | None) -> None:
+        if example_moe is None:
+            raise RuntimeError(
+                "No Param2MoEMoEBlock found in model.layers. "
+                "Check first_k_dense_replace and num_experts in config."
+            )
+        self.num_logical_experts = example_moe.num_experts
+        self.num_routed_experts = example_moe.num_experts
+        self.num_shared_experts = example_moe.num_shared_experts
+
+        self.num_physical_experts = self.num_logical_experts
+        self.num_local_physical_experts = self.num_logical_experts
+        self.num_redundant_experts = 0
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+
+        for moe in self.moe_mlp_layers:
+            moe.n_physical_experts = num_physical_experts
+            moe.n_local_physical_experts = num_local_physical_experts
+            moe.n_redundant_experts = self.num_redundant_experts
+
+            fused = moe.experts
+            if hasattr(fused, "n_local_physical_experts"):
+                fused.n_local_physical_experts = num_local_physical_experts
+            if hasattr(fused, "n_physical_experts"):
+                fused.n_physical_experts = num_physical_experts
+            if hasattr(fused, "n_redundant_experts"):
+                fused.n_redundant_experts = self.num_redundant_experts
+            if hasattr(fused, "update_expert_map"):
+                fused.update_expert_map()
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        self.expert_weights.clear()
+        for layer_idx, layer in enumerate(self.moe_layers):
+            if hasattr(layer, "get_expert_weights"):
+                self.expert_weights.append(layer.get_expert_weights())
+            if hasattr(layer, "set_eplb_state"):
+                layer.set_eplb_state(
+                    moe_layer_idx=layer_idx,
+                    expert_load_view=expert_load_view,
+                    logical_to_physical_map=logical_to_physical_map,
+                    logical_replica_count=logical_replica_count,
+                )
+
+
+class Param2MoEForCausalLM(
+    nn.Module, SupportsPP, SupportsLoRA, Param2MoEMixtureOfExperts
+):
+    """
+    vLLM-native Param2MoE CausalLM.
+
+    Uses Grouped-Query Attention (GQA) with a Sigmoid-scored,
+    grouped-topk Mixture-of-Experts MLP.
+    """
+
+    # LoRA packed-module mapping. The fused gate_up_proj handles
+    # gate_proj and up_proj from the checkpoint.
+    packed_modules_mapping = {
+        "qkv_proj": ["query_key_value"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    # Modules eligible for LoRA adaptation.
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+
+    # Embedding layers and their weight-tying counterparts.
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+
+    # Modules that need vocab-size padding for LoRA.
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        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 = Param2MoEModel(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+
+        self.tie_word_embeddings: bool = getattr(config, "tie_word_embeddings", False)
+        if get_pp_group().is_last_rank:
+            if self.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    config.vocab_size,
+                    config.hidden_size,
+                    quant_config=quant_config,
+                    prefix=maybe_prefix(prefix, "lm_head"),
+                )
+            self.logits_processor = LogitsProcessor(config.vocab_size)
+        else:
+            self.lm_head = PPMissingLayer()
+            self.logits_processor = None  # type: ignore[assignment]
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+        self.expert_weights: list[torch.Tensor] = []
+        self.num_moe_layers: int = 0
+        self.moe_layers: list = []
+        self.moe_mlp_layers: list = []
+
+        example_moe: Param2MoEMoEBlock | None = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+            if isinstance(layer.mlp, Param2MoEMoEBlock):
+                example_moe = layer.mlp
+                self.moe_mlp_layers.append(layer.mlp)
+                self.moe_layers.append(layer.mlp.experts)
+                self.num_moe_layers += 1
+
+        if self.config.num_experts is not None:
+            self.extract_moe_parameters(example_moe)
+
+    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:
+        return self.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        if not get_pp_group().is_last_rank:
+            return None
+        return self.logits_processor(self.lm_head, hidden_states)
+
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(_rename_and_normalize_weights(weights))

vllm/model_executor/models/registry.py

@@ -182,6 +182,7 @@ _TEXT_GENERATION_MODELS = {
     "PanguEmbeddedForCausalLM": ("openpangu", "PanguEmbeddedForCausalLM"),
     "PanguProMoEV2ForCausalLM": ("openpangu", "PanguProMoEV2ForCausalLM"),
     "PanguUltraMoEForCausalLM": ("openpangu", "PanguUltraMoEForCausalLM"),
+    "Param2MoEForCausalLM": ("param2moe", "Param2MoEForCausalLM"),
     "PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"),
     "PhiForCausalLM": ("phi", "PhiForCausalLM"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),