Create sarvam.py

sarvam.py

@@ -0,0 +1,788 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Copyright 2026 Sarvam AI team. All rights reserved.
+#
+# This code is based on Llama, Deepseek, and Bailing MoE implementations
+# in this library. It has been modified from its original forms to
+# accommodate Sarvam's MoE architectures.
+#
+# 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.
+
+from __future__ import annotations
+
+import math
+from collections.abc import Iterable, Iterator
+from itertools import islice
+
+import torch
+from torch import nn
+
+from vllm.config import CacheConfig, ParallelConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
+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 .bailing_moe import BailingMoeForCausalLM
+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 yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
+def _is_gate_expert_bias_name(name: str) -> bool:
+    return name.endswith(".mlp.gate.e_score_correction_bias") or name.endswith(
+        ".gate.e_score_correction_bias"
+    )
+
+
+def _zero_mean_tensor(t: torch.Tensor) -> torch.Tensor:
+    if t.numel() == 0:
+        return t
+    return t - t.mean()
+
+
+def _normalized_weights(
+    weights: Iterable[tuple[str, torch.Tensor]],
+) -> Iterator[tuple[str, torch.Tensor]]:
+    for name, w in weights:
+        if _is_gate_expert_bias_name(name):
+            yield name, _zero_mean_tensor(w)
+        else:
+            yield name, w
+
+
+class SarvamMLAAttention(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        config,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.qk_nope_head_dim = config.qk_nope_head_dim
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
+        self.v_head_dim = config.v_head_dim
+
+        self.q_lora_rank = getattr(config, "q_lora_rank", None)
+        self.kv_lora_rank = config.kv_lora_rank
+
+        self.total_num_heads = config.num_attention_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_size == 0
+        self.num_local_heads = self.total_num_heads // tp_size
+
+        self.scaling = self.qk_head_dim**-0.5
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if self.q_lora_rank is not None:
+            self.q_a_proj = ReplicatedLinear(
+                self.hidden_size,
+                self.q_lora_rank,
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.q_a_proj",
+            )
+            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
+            self.q_b_proj = ColumnParallelLinear(
+                self.q_lora_rank,
+                self.total_num_heads * self.qk_head_dim,
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.q_b_proj",
+            )
+            self.q_proj = None  # type: ignore
+        else:
+            self.q_proj = ColumnParallelLinear(
+                self.hidden_size,
+                self.total_num_heads * self.qk_head_dim,
+                bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.q_proj",
+            )
+            self.q_a_proj = None  # type: ignore
+            self.q_a_layernorm = None  # type: ignore
+            self.q_b_proj = None  # type: ignore
+
+        # KV latent (MQA-style) A-proj
+        self.kv_a_proj_with_mqa = ReplicatedLinear(
+            self.hidden_size,
+            self.kv_lora_rank + self.qk_rope_head_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.kv_a_proj_with_mqa",
+        )
+        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
+
+        # KV B-proj produces per-head K_nope and V
+        self.kv_b_proj = ColumnParallelLinear(
+            self.kv_lora_rank,
+            self.total_num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.kv_b_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.rotary_emb = get_rope(
+            self.qk_rope_head_dim,
+            # rotary_dim=self.qk_rope_head_dim,
+            max_position=config.max_position_embeddings,
+            rope_parameters=config.rope_parameters,
+            is_neox_style=False,
+        )
+
+        if config.rope_parameters.get("rope_type", None) == "deepseek_yarn":
+            mscale_all_dim = config.rope_parameters.get("mscale_all_dim", False)
+            scaling_factor = config.rope_parameters["factor"]
+            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
+            self.scaling = self.scaling * mscale * mscale
+
+        mla_modules = MLAModules(
+            kv_a_layernorm=self.kv_a_layernorm,
+            kv_b_proj=self.kv_b_proj,
+            rotary_emb=self.rotary_emb,
+            o_proj=self.o_proj,
+            fused_qkv_a_proj=None,
+            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
+            q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None,
+            q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
+            q_proj=self.q_proj if self.q_lora_rank is None else None,
+            indexer=None,
+            indexer_rotary_emb=None,
+            is_sparse=False,
+            topk_indices_buffer=None,
+        )
+
+        self.mla_attn = MultiHeadLatentAttentionWrapper(
+            self.hidden_size,
+            self.num_local_heads,
+            self.scaling,
+            self.qk_nope_head_dim,
+            self.qk_rope_head_dim,
+            self.v_head_dim,
+            self.q_lora_rank,
+            self.kv_lora_rank,
+            mla_modules,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.mla_attn(positions, hidden_states, llama_4_scaling=None)
+
+
+class SarvamMLAMLP(nn.Module):
+    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(
+            config.hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_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 SarvamMLAMoE(nn.Module):
+    def __init__(
+        self,
+        config,
+        parallel_config: ParallelConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.hidden_size = config.hidden_size
+
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 2.5)
+
+        self.n_group = getattr(config, "n_group", None)
+        self.topk_group = getattr(config, "topk_group", None)
+        self.use_grouped_topk = self.n_group is not None and self.topk_group is not None
+
+        self.norm_expert_prob = getattr(config, "norm_topk_prob", True)
+
+        router_dtype_cfg = getattr(config, "router_dtype", "fp32")
+        if router_dtype_cfg is None:
+            self.router_dtype = None
+        elif router_dtype_cfg == "fp32":
+            self.router_dtype = torch.float32
+        else:
+            self.router_dtype = torch.bfloat16
+
+        self.gate = nn.Linear(
+            self.hidden_size,
+            self.num_experts,
+            bias=False,
+            dtype=self.router_dtype,
+        )
+
+        if getattr(config, "moe_router_enable_expert_bias", True):
+            self.gate.e_score_correction_bias = nn.Parameter(
+                torch.empty(
+                    (self.num_experts,),
+                    dtype=torch.float32,
+                )
+            )
+        else:
+            self.gate.e_score_correction_bias = None
+
+        self.score_function = getattr(config, "score_function", "sigmoid")
+        self.num_shared_experts = getattr(config, "num_shared_experts", 1)
+        if self.num_shared_experts > 0:
+            if hasattr(config, "moe_shared_expert_intermediate_size"):
+                shared_int = config.moe_shared_expert_intermediate_size
+            else:
+                shared_int = config.moe_intermediate_size
+            shared_int *= self.num_shared_experts
+            self.shared_experts = SarvamMLAMLP(
+                intermediate_size=shared_int,
+                config=config,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=f"{prefix}.shared_experts",
+            )
+        else:
+            self.shared_experts = None
+
+        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_logits = self.gate(
+            hidden_states.to(self.router_dtype)
+            if self.router_dtype is not None
+            else hidden_states
+        )
+        router_logits = router_logits.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
+
+        # expert_output *= self.routed_scaling_factor
+
+        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 SarvamMLABlock(nn.Module):
+    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
+        parallel_config = vllm_config.parallel_config
+        layer_idx = int(prefix.split(".")[-1])
+        hidden_size = config.hidden_size
+        dense_intermediate = getattr(config, "intermediate_size", 16384)
+
+        self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = SarvamMLAAttention(
+            vllm_config=vllm_config,
+            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)
+        use_moe = hasattr(config, "num_experts") and config.num_experts is not None
+        first_k_dense = getattr(config, "first_k_dense_replace", 1)
+        moe_layer_freq = getattr(config, "moe_layer_freq", 1)
+        if use_moe:
+            is_moe_layer = layer_idx >= first_k_dense and (
+                (layer_idx - first_k_dense) % moe_layer_freq == 0
+            )
+        else:
+            is_moe_layer = False
+
+        if is_moe_layer:
+            self.mlp = SarvamMLAMoE(
+                config=config,
+                parallel_config=parallel_config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            self.mlp = SarvamMLAMLP(
+                intermediate_size=dense_intermediate,
+                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]:
+        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
+
+
+class SarvamMLAModel(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 = getattr(config, "tie_word_embeddings", False)
+        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.embedding_dropout = torch.nn.Dropout(
+            getattr(config, "embedding_dropout", 0.0)
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: SarvamMLABlock(
+                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)
+            hidden_states = self.embedding_dropout(hidden_states)
+            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 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,
+        )
+
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        """Load weights with stacked gate+up and MoE expert remapping."""
+        weights = _normalized_weights(weights)
+        stacked_params_mapping = [
+            ("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:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    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)
+                break
+            else:
+                mapped = 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)
+                    mapped = True
+                    break
+
+                if mapped:
+                    continue
+
+                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)
+                weight_loader(param, loaded_weight)
+                loaded_params.add(name)
+
+        return loaded_params
+
+
+class SarvamMixtureOfExperts(MixtureOfExperts):
+    def extract_moe_parameters(self, example_moe: SarvamMLAMoE | None) -> None:
+        if example_moe is None:
+            raise RuntimeError("No SarvamMLAMoE layer found in model.layers.")
+
+        self.num_logical_experts = example_moe.num_experts
+        self.num_routed_experts = example_moe.num_experts  # routed pool size
+        self.num_shared_experts = getattr(example_moe.config, "num_shared_experts", 1)
+
+        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, eplb_state) -> None:
+        self.eplb_state = eplb_state
+        for moe in self.moe_layers:
+            if hasattr(moe, "set_eplb_state"):
+                moe.set_eplb_state(eplb_state)
+
+
+class SarvamMLAForCausalLM(nn.Module, SupportsPP, SupportsLoRA, SarvamMixtureOfExperts):
+    packed_modules_mapping = {
+        "q_proj": ["q_proj"],
+        "q_a_proj": ["q_a_proj"],
+        "q_b_proj": ["q_b_proj"],
+        "kv_a_proj_with_mqa": ["kv_a_proj_with_mqa"],
+        "kv_b_proj": ["kv_b_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    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 = SarvamMLAModel(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+
+        self.tie_word_embeddings = 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
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+        self.expert_weights = []
+        self.num_moe_layers = 0
+
+        self.moe_layers = []
+        self.moe_mlp_layers = []
+
+        example_moe = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+            if isinstance(layer.mlp, SarvamMLAMoE):
+                example_moe = layer.mlp
+                self.moe_mlp_layers.append(layer.mlp)
+                self.moe_layers.append(layer.mlp.experts)
+                self.num_moe_layers += 1
+
+        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
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."] if self.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+
+class SarvamMoEForCausalLM(BailingMoeForCausalLM):
+    """Same as BailingMoeForCausalLM, but normalizes gate expert_bias pre-load."""
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        return super().load_weights(_normalized_weights(weights))