"""
Copyright 2023-2024 SGLang Team
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.
"""

# Adapted from:
# https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
"""Inference-only DeepseekV2 model."""
from typing import Any, Dict, Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig
from vllm.distributed import (
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    get_tp_group,
    tensor_model_parallel_all_reduce,
)
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.model_loader.weight_utils import default_weight_loader

from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.utils import is_flashinfer_available

if is_flashinfer_available():
    from flashinfer import bmm_fp8


class DeepseekV2MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = True,
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
        )
        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 DeepseekV2MoE(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_shared_experts = config.n_shared_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        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."
            )

        self.experts = FusedMoE(
            num_experts=config.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
        )

        self.gate = ReplicatedLinear(
            config.hidden_size, config.n_routed_experts, bias=False, quant_config=None
        )
        if config.n_shared_experts is not None:
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
            self.shared_experts = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
            )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)
        if self.n_shared_experts is not None:
            shared_output = self.shared_experts(hidden_states)
        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)
        final_hidden_states = (
            self.experts(hidden_states=hidden_states, router_logits=router_logits)
            * self.routed_scaling_factor
        )
        if shared_output is not None:
            final_hidden_states = final_hidden_states + shared_output
        if self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)

        return final_hidden_states.view(num_tokens, hidden_dim)


def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
    import math

    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


def input_to_float8(x, dtype=torch.float8_e4m3fn):
    finfo = torch.finfo(dtype)
    min_val, max_val = x.aminmax()
    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
    scale = finfo.max / amax
    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()


class DeepseekV2Attention(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int,
        kv_lora_rank: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        cache_config=None,
        quant_config: Optional[QuantizationConfig] = None,
        layer_id=None,
    ) -> None:
        super().__init__()
        self.layer_id = layer_id
        self.hidden_size = hidden_size
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.num_heads = num_heads
        tp_size = get_tensor_model_parallel_world_size()
        assert num_heads % tp_size == 0
        self.num_local_heads = num_heads // tp_size
        self.scaling = self.qk_head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = 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,
            )
            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
            self.q_b_proj = ColumnParallelLinear(
                q_lora_rank,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
            )
        else:
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
            )

        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,
        )
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
            quant_config=quant_config,
        )
        # O projection.
        self.o_proj = RowParallelLinear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        rope_scaling["rope_type"] = "deepseek_yarn"
        self.rotary_emb = get_rope(
            qk_rope_head_dim,
            rotary_dim=qk_rope_head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
            is_neox_style=False,
        )

        if rope_scaling:
            mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
            scaling_factor = rope_scaling["factor"]
            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
            self.scaling = self.scaling * mscale * mscale

        # TODO, support head_size 192
        self.attn = RadixAttention(
            self.num_local_heads,
            256,
            self.scaling,
            num_kv_heads=self.num_local_heads,
            layer_id=layer_id,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        if self.q_lora_rank is not None:
            q = self.q_a_proj(hidden_states)[0]
            q = self.q_a_layernorm(q)
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
        else:
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
        _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
        kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        latent_cache = latent_cache.unsqueeze(1)
        kv_a = self.kv_a_layernorm(kv_a.contiguous())
        kv = self.kv_b_proj(kv_a)[0]
        kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
        k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
        k_pe = latent_cache[:, :, self.kv_lora_rank :]
        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
        q[..., self.qk_nope_head_dim :] = q_pe
        k = torch.empty_like(q)
        k[..., : self.qk_nope_head_dim] = k_nope
        k[..., self.qk_nope_head_dim :] = k_pe
        q = torch.nn.functional.pad(q, [0, 256 - self.qk_head_dim], value=0).view(
            -1, self.num_local_heads * 256
        )
        k = torch.nn.functional.pad(k, [0, 256 - self.qk_head_dim], value=0).view(
            -1, self.num_local_heads * 256
        )
        v = torch.nn.functional.pad(v, [0, 256 - self.v_head_dim], value=0).view(
            -1, self.num_local_heads * 256
        )
        attn_output = self.attn(q, k, v, forward_batch)
        attn_output = attn_output.view(-1, self.num_local_heads, 256)[
            ..., : self.v_head_dim
        ].reshape(-1, self.num_local_heads * self.v_head_dim)
        output, _ = self.o_proj(attn_output)
        return output


class DeepseekV2AttentionMLA(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int,
        kv_lora_rank: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        cache_config=None,
        quant_config: Optional[QuantizationConfig] = None,
        layer_id=None,
        use_dp=False,
    ) -> None:
        super().__init__()
        self.layer_id = layer_id
        self.hidden_size = hidden_size
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.num_heads = num_heads
        tp_size = get_tensor_model_parallel_world_size()
        assert num_heads % tp_size == 0
        self.num_local_heads = num_heads if use_dp else num_heads // tp_size
        self.scaling = self.qk_head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

        if use_dp:
            # For data parallel attention
            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,
                )
                self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
                self.q_b_proj = ReplicatedLinear(
                    q_lora_rank,
                    self.num_heads * self.qk_head_dim,
                    bias=False,
                    quant_config=quant_config,
                )
            else:
                self.q_proj = ReplicatedLinear(
                    self.hidden_size,
                    self.num_heads * self.qk_head_dim,
                    bias=False,
                    quant_config=quant_config,
                )
            self.kv_b_proj = ReplicatedLinear(
                self.kv_lora_rank,
                self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
                bias=False,
                quant_config=quant_config,
            )
            # O projection.
            self.o_proj = ReplicatedLinear(
                self.num_heads * self.v_head_dim,
                self.hidden_size,
                bias=False,
                quant_config=quant_config,
            )
        else:
            # For tensor parallel attention
            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,
                )
                self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
                self.q_b_proj = ColumnParallelLinear(
                    q_lora_rank,
                    self.num_heads * self.qk_head_dim,
                    bias=False,
                    quant_config=quant_config,
                )
            else:
                self.q_proj = ColumnParallelLinear(
                    self.hidden_size,
                    self.num_heads * self.qk_head_dim,
                    bias=False,
                    quant_config=quant_config,
                )
            self.kv_b_proj = ColumnParallelLinear(
                self.kv_lora_rank,
                self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
                bias=False,
                quant_config=quant_config,
            )
            # O projection.
            self.o_proj = RowParallelLinear(
                self.num_heads * self.v_head_dim,
                self.hidden_size,
                bias=False,
                quant_config=quant_config,
            )

        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,
        )
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
        rope_scaling["rope_type"] = "deepseek_yarn"
        self.rotary_emb = get_rope(
            qk_rope_head_dim,
            rotary_dim=qk_rope_head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
            is_neox_style=False,
        )

        if rope_scaling:
            mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
            scaling_factor = rope_scaling["factor"]
            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
            self.scaling = self.scaling * mscale * mscale

        self.attn = RadixAttention(
            self.num_local_heads,
            self.kv_lora_rank + self.qk_rope_head_dim,
            self.scaling,
            num_kv_heads=1,
            layer_id=layer_id,
            v_head_dim=self.kv_lora_rank,
        )

        self.w_kc = None
        self.w_vc = None
        self.w_scale = None

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        q_len = hidden_states.shape[0]
        q_input = hidden_states.new_empty(
            q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim
        )
        if self.q_lora_rank is not None:
            q = self.q_a_proj(hidden_states)[0]
            q = self.q_a_layernorm(q)
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
        else:
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
        q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

        if self.w_kc.dtype == torch.float8_e4m3fn:
            q_nope_val, q_nope_scale = input_to_float8(
                q_nope.transpose(0, 1), torch.float8_e4m3fn
            )
            q_nope_out = bmm_fp8(
                q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
            )
        else:
            q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
        q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1)

        latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
        v_input = latent_cache[..., : self.kv_lora_rank]
        v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1)
        k_input = latent_cache.unsqueeze(1)
        k_input[..., : self.kv_lora_rank] = v_input
        k_pe = k_input[..., self.kv_lora_rank :]

        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
        q_input[..., self.kv_lora_rank :] = q_pe
        k_input[..., self.kv_lora_rank :] = k_pe

        attn_output = self.attn(q_input, k_input, v_input, forward_batch)
        attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)

        if self.w_vc.dtype == torch.float8_e4m3fn:
            attn_output_val, attn_output_scale = input_to_float8(
                attn_output.transpose(0, 1), torch.float8_e4m3fn
            )
            attn_bmm_output = bmm_fp8(
                attn_output_val,
                self.w_vc,
                attn_output_scale,
                self.w_scale,
                torch.bfloat16,
            )
        else:
            attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
        attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
        output, _ = self.o_proj(attn_output)

        return output


def all_gather(
    input_tensor: torch.Tensor, forward_batch: ForwardBatch, rank, world_size, group
):
    if world_size == 1:
        return input_tensor

    all_lens = forward_batch.global_num_tokens
    max_len = max(forward_batch.global_num_tokens)

    padded_tensor = torch.nn.functional.pad(
        input_tensor, (0, 0, 0, max_len - input_tensor.shape[0])
    )

    torch.distributed.all_gather_into_tensor(
        forward_batch.gathered_buffer, padded_tensor, group=group
    )

    gathered_tensors = torch.concat(
        [
            forward_batch.gathered_buffer[i * max_len : i * max_len + all_lens[i]]
            for i in range(world_size)
        ]
    )

    start_index = 0 if rank == 0 else sum(all_lens[:rank])
    end_index = start_index + all_lens[rank]

    return gathered_tensors, start_index, end_index


class DeepseekV2DecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        cache_config=None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        self.enable_dp_attention = (
            not global_server_args_dict["disable_mla"]
            and global_server_args_dict["enable_dp_attention"]
        )
        if self.enable_dp_attention:
            self.tp_rank = get_tensor_model_parallel_rank()
            self.tp_size = get_tensor_model_parallel_world_size()
            self.tp_group = get_tp_group().device_group
        if not global_server_args_dict["disable_mla"]:
            self.self_attn = DeepseekV2AttentionMLA(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                qk_nope_head_dim=config.qk_nope_head_dim,
                qk_rope_head_dim=config.qk_rope_head_dim,
                v_head_dim=config.v_head_dim,
                q_lora_rank=(
                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
                ),
                kv_lora_rank=config.kv_lora_rank,
                rope_theta=rope_theta,
                rope_scaling=rope_scaling,
                max_position_embeddings=max_position_embeddings,
                cache_config=cache_config,
                quant_config=quant_config,
                layer_id=layer_id,
                use_dp=self.enable_dp_attention,
            )
        else:
            self.self_attn = DeepseekV2Attention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                qk_nope_head_dim=config.qk_nope_head_dim,
                qk_rope_head_dim=config.qk_rope_head_dim,
                v_head_dim=config.v_head_dim,
                q_lora_rank=(
                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
                ),
                kv_lora_rank=config.kv_lora_rank,
                rope_theta=rope_theta,
                rope_scaling=rope_scaling,
                max_position_embeddings=max_position_embeddings,
                cache_config=cache_config,
                quant_config=quant_config,
                layer_id=layer_id,
            )
        if (
            config.n_routed_experts is not None
            and layer_id >= config.first_k_dense_replace
            and layer_id % config.moe_layer_freq == 0
        ):
            self.mlp = DeepseekV2MoE(config=config, quant_config=quant_config)
        else:
            self.mlp = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
            )
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
    ) -> torch.Tensor:
        # Self Attention
        if not forward_batch.forward_mode.is_idle():
            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,
                forward_batch=forward_batch,
            )
            hidden_states, residual = self.post_attention_layernorm(
                hidden_states, residual
            )

        # Fully Connected
        if self.enable_dp_attention:
            hidden_states, start_idx, end_idx = all_gather(
                hidden_states, forward_batch, self.tp_rank, self.tp_size, self.tp_group
            )
            hidden_states = self.mlp(hidden_states)
            hidden_states = hidden_states[start_idx:end_idx]
        else:
            hidden_states = self.mlp(hidden_states)

        return hidden_states, residual


class DeepseekV2Model(nn.Module):

    fall_back_to_pt_during_load = False

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config=None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.padding_id = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            enable_tp=not global_server_args_dict["enable_dp_attention"],
        )
        self.layers = nn.ModuleList(
            [
                DeepseekV2DecoderLayer(
                    config,
                    layer_id,
                    cache_config=cache_config,
                    quant_config=quant_config,
                )
                for layer_id in range(config.num_hidden_layers)
            ]
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        hidden_states = self.embed_tokens(input_ids)
        residual = None
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions, hidden_states, forward_batch, residual
            )
        if not forward_batch.forward_mode.is_idle():
            hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class DeepseekV2ForCausalLM(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config=None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.model = DeepseekV2Model(config, cache_config, quant_config)
        if global_server_args_dict["enable_dp_attention"]:
            self.lm_head = ReplicatedLinear(
                config.hidden_size,
                config.vocab_size,
                bias=False,
            )
            self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size, config.hidden_size, quant_config=quant_config
            )
            self.logits_processor = LogitsProcessor(config)

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, forward_batch)
        if not forward_batch.forward_mode.is_idle():
            return self.logits_processor(
                input_ids, hidden_states, self.lm_head.weight, forward_batch
            )

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = 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,
        )

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            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)
                # 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:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue

                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)

        if not global_server_args_dict["disable_mla"]:
            for layer_id in range(self.config.num_hidden_layers):
                self_attn = self.model.layers[layer_id].self_attn
                w_kc, w_vc = self_attn.kv_b_proj.weight.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 hasattr(self_attn.kv_b_proj, "weight_scale"):
                    self_attn.w_scale = self_attn.kv_b_proj.weight_scale
                del self_attn.kv_b_proj


EntryClass = DeepseekV2ForCausalLM
