# Apache License, Version 2.0:
# 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.
#
# MIT License:
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import concurrent.futures
import logging
import os
from enum import IntEnum, auto
from typing import Any, Dict, Iterable, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm

from sglang.srt.configs import LongcatFlashConfig
from sglang.srt.distributed import (
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_reduce,
)
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.amx_utils import PackWeightMethod
from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
from sglang.srt.layers.dp_attention import (
    get_attention_tp_rank,
    get_attention_tp_size,
    is_dp_attention_enabled,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    MergedColumnParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe.ep_moe.kernels import zero_experts_compute_triton
from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, get_moe_impl_class
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
from sglang.srt.layers.moe.topk import StandardTopKOutput, TopK
from sglang.srt.layers.quantization import deep_gemm_wrapper
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
from sglang.srt.layers.quantization.fp8_utils import (
    block_quant_dequant,
    block_quant_to_tensor_quant,
    channel_quant_to_tensor_quant,
    normalize_e4m3fn_to_e4m3fnuz,
    requant_weight_ue8m0_inplace,
)
from sglang.srt.layers.quantization.int8_utils import (
    block_dequant as int8_block_dequant,
)
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.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA
from sglang.srt.utils import (
    BumpAllocator,
    LazyValue,
    add_prefix,
    bind_or_assign,
    cpu_has_amx_support,
    get_bool_env_var,
    get_device_sm,
    get_int_env_var,
    is_cpu,
    is_cuda,
    is_flashinfer_available,
    is_hip,
    is_non_idle_and_non_empty,
    is_npu,
    is_sm100_supported,
)

_is_hip = is_hip()
_is_cuda = is_cuda()
_is_npu = is_npu()
_is_fp8_fnuz = is_fp8_fnuz()
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
_is_cpu_amx_available = cpu_has_amx_support()
_is_cpu = is_cpu()
_device_sm = get_device_sm()

if _is_cuda:
    from sgl_kernel import (
        awq_dequantize,
        bmm_fp8,
        dsv3_fused_a_gemm,
        dsv3_router_gemm,
        merge_state_v2,
    )
elif _is_cpu and _is_cpu_amx_available:
    pass
elif _is_hip:
    from sglang.srt.layers.quantization.awq_triton import (
        awq_dequantize_triton as awq_dequantize,
    )
else:
    from vllm._custom_ops import awq_dequantize

logger = logging.getLogger(__name__)


class LongcatFlashMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = False,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("gate_up_proj", prefix),
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=add_prefix("down_proj", prefix),
        )
        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 LongcatFlashRouter(nn.Module):
    def __init__(
        self,
        config,
        zero_expert_num=0,
        rounter_params_dtype=torch.float32,
        prefix: str = "",
    ):
        super().__init__()
        self.n_routed_experts = config.n_routed_experts
        self.n_routed_experts = self.n_routed_experts + zero_expert_num
        self.rounter_params_dtype = rounter_params_dtype
        self.classifier = ReplicatedLinear(
            config.hidden_size,
            self.n_routed_experts,
            bias=config.router_bias,
            params_dtype=rounter_params_dtype,
            quant_config=None,
            prefix=add_prefix("classifier", prefix),
        )
        self.e_score_correction_bias = nn.Parameter(
            torch.zeros((self.n_routed_experts), dtype=rounter_params_dtype)
        )

    def forward(self, hidden_states):
        logits, _ = self.classifier(hidden_states.to(self.rounter_params_dtype))
        return logits


class LongcatFlashMoE(nn.Module):

    def __init__(
        self,
        config: LongcatFlashConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.layer_id = layer_id
        self.routed_scaling_factor = config.routed_scaling_factor
        self.num_experts = config.n_routed_experts
        self.top_k = config.moe_topk
        self.zero_expert_num = config.zero_expert_num
        self.zero_expert_type = config.zero_expert_type

        if config.rounter_params_dtype == "float32":
            self.rounter_params_dtype = torch.float32
        else:
            self.rounter_params_dtype = torch.bfloat16

        self.tp_size = get_tensor_model_parallel_world_size()

        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.router = LongcatFlashRouter(
            config=self.config,
            zero_expert_num=self.zero_expert_num,
            rounter_params_dtype=self.rounter_params_dtype,
            prefix=add_prefix("router", prefix),
        )

        self.topk = TopK(
            top_k=self.top_k,
            renormalize=False,
            use_grouped_topk=False,
            correction_bias=self.router.e_score_correction_bias.data,
        )
        self.topk.forward = self.topk.forward_native

        self.experts = get_moe_impl_class()(
            num_experts=self.num_experts,
            top_k=self.top_k,
            layer_id=self.layer_id,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            quant_config=quant_config,
            prefix=add_prefix("experts", prefix),
        )

    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: (num_tokens, n_experts)
        router_logits = self.router(hidden_states)
        topk_weights, topk_idx, _ = self.topk(
            hidden_states,
            router_logits,
        )
        if self.zero_expert_type is not None:
            zero_expert_result = zero_experts_compute_triton(
                expert_indices=topk_idx,
                expert_scales=topk_weights,
                num_experts=self.num_experts,
                zero_expert_type=self.zero_expert_type,
                hidden_states=hidden_states,
            )
        topk_output = StandardTopKOutput(topk_weights, topk_idx, _)

        final_hidden_states = self.experts(hidden_states, topk_output)
        final_hidden_states *= self.routed_scaling_factor

        if self.zero_expert_type is not None and hidden_states.shape[0] > 0:
            final_hidden_states += zero_expert_result.to(final_hidden_states.device)

        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 get_moe_weights(self):
        return [
            x.data
            for name, x in self.experts.named_parameters()
            if name not in ["correction_bias"]
        ]


class LongcatFlashDecoderLayer(nn.Module):

    def __init__(
        self,
        config: LongcatFlashConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_id = layer_id
        self.alt_stream = alt_stream
        self.self_attn = nn.ModuleList(
            [
                DeepseekV2AttentionMLA(
                    config=config,
                    hidden_size=config.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,
                    kv_lora_rank=config.kv_lora_rank,
                    rope_theta=config.rope_theta,
                    rope_scaling=None,
                    max_position_embeddings=config.max_position_embeddings,
                    quant_config=(
                        None
                        if "self_attn" in getattr(config, "disable_quant_module", [])
                        else quant_config
                    ),
                    layer_id=layer_id * 2 + i,
                    reduce_results=False,
                    prefix=add_prefix(f"self_attn.{i}", prefix),
                    alt_stream=self.alt_stream,
                )
                for i in range(2)
            ]
        )

        self.input_layernorm = nn.ModuleList(
            [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
        )
        self.post_attention_layernorm = nn.ModuleList(
            [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
        )

        self.mlps = nn.ModuleList(
            [
                LongcatFlashMLP(
                    hidden_size=config.hidden_size,
                    intermediate_size=config.intermediate_size,
                    hidden_act=config.hidden_act,
                    quant_config=(
                        None
                        if "mlps" in getattr(config, "disable_quant_module", [])
                        else quant_config
                    ),
                    prefix=add_prefix(f"mlps.{i}", prefix),
                )
                for i in range(2)
            ]
        )

        self.mlp = LongcatFlashMoE(
            layer_id=self.layer_id,
            config=config,
            quant_config=quant_config,
            prefix=add_prefix("mlp", prefix),
        )

        self.attn_tp_size = get_attention_tp_size()
        self.attn_tp_rank = get_attention_tp_rank()

        self.mlp_layer_scatter_modes = [
            LayerScatterModes.init_new(
                layer_id=self.layer_id * 2 + i,
                num_layers=config.num_hidden_layers,
                is_layer_sparse=False,
                is_previous_layer_sparse=False,
            )
            for i in range(2)
        ]
        self.mlp_layer_communicator = [
            LayerCommunicator(
                layer_scatter_modes=self.mlp_layer_scatter_modes[i],
                input_layernorm=self.input_layernorm[i],
                post_attention_layernorm=self.post_attention_layernorm[i],
            )
            for i in range(2)
        ]

        self.moe_layer_scatter_modes = LayerScatterModes.init_new(
            layer_id=self.layer_id,
            num_layers=config.num_hidden_layers,
            is_layer_sparse=True,
            is_previous_layer_sparse=True,
        )
        self.moe_layer_communicator = LayerCommunicator(
            layer_scatter_modes=self.moe_layer_scatter_modes,
            input_layernorm=self.input_layernorm[0],
            post_attention_layernorm=self.post_attention_layernorm[0],
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
        zero_allocator: BumpAllocator,
    ) -> torch.Tensor:
        # first_attn
        hidden_states, residual = self.moe_layer_communicator.prepare_attn(
            hidden_states, residual, forward_batch
        )
        if hidden_states.shape[0] != 0:
            hidden_states = self.self_attn[0](
                positions=positions,
                hidden_states=hidden_states,
                forward_batch=forward_batch,
                zero_allocator=zero_allocator,
            )

        # moe
        hidden_states, residual = self.moe_layer_communicator.prepare_mlp(
            hidden_states, residual, forward_batch
        )
        moe_hidden_states = hidden_states.clone()
        moe_residual = residual.clone()
        moe_hidden_states = self.mlp(moe_hidden_states)
        moe_hidden_states, moe_residual = self.moe_layer_communicator.postprocess_layer(
            moe_hidden_states, moe_residual, forward_batch
        )

        hidden_states, residual = self.forward_mlp(
            hidden_states, positions, residual, forward_batch, zero_allocator
        )

        hidden_states = moe_hidden_states + hidden_states
        return hidden_states, residual

    def forward_mlp(
        self, hidden_states, positions, residual, forward_batch, zero_allocator
    ):
        # first_mlp
        hidden_states = self.mlps[0](hidden_states)
        # TP all_reduce
        hidden_states = tensor_model_parallel_all_reduce(hidden_states)

        # second_attn
        hidden_states, residual = self.mlp_layer_communicator[1].prepare_attn(
            hidden_states, residual, forward_batch
        )
        if hidden_states.shape[0] != 0:
            hidden_states = self.self_attn[1](
                positions=positions,
                hidden_states=hidden_states,
                forward_batch=forward_batch,
                zero_allocator=zero_allocator,
            )

        # second_mlp
        hidden_states, residual = self.mlp_layer_communicator[1].prepare_mlp(
            hidden_states, residual, forward_batch
        )
        hidden_states = self.mlps[1](hidden_states)
        # TP all_reduce
        hidden_states = tensor_model_parallel_all_reduce(hidden_states)

        hidden_states, residual = self.mlp_layer_communicator[1].postprocess_layer(
            hidden_states, residual, forward_batch
        )

        return hidden_states, residual


class LongcatFlashModel(nn.Module):
    fall_back_to_pt_during_load = False

    def __init__(
        self,
        config: LongcatFlashConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            enable_tp=not is_dp_attention_enabled(),
        )

        self.alt_stream = torch.cuda.Stream()
        self.layers = nn.ModuleList(
            [
                LongcatFlashDecoderLayer(
                    config,
                    layer_id,
                    quant_config=quant_config,
                    prefix=add_prefix(f"layers.{layer_id}", prefix),
                    alt_stream=self.alt_stream,
                )
                for layer_id in range(config.num_hidden_layers)
            ]
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def get_input_embeddings(self) -> torch.Tensor:
        return self.embed_tokens

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        total_num_layers = len(self.layers)
        device = input_embeds.device if input_embeds is not None else input_ids.device
        zero_allocator = BumpAllocator(
            buffer_size=total_num_layers * 2 * (2 if forward_batch.can_run_tbo else 1),
            dtype=torch.float32,
            device=device,
        )
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
        else:
            hidden_states = input_embeds

        residual = None

        for i in range(total_num_layers):
            with get_global_expert_distribution_recorder().with_current_layer(i):
                layer = self.layers[i]
                hidden_states, residual = layer(
                    positions, hidden_states, forward_batch, residual, zero_allocator
                )

        if hidden_states.shape[0] != 0:
            if residual is None:
                hidden_states = self.norm(hidden_states)
            else:
                hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class LongcatFlashForCausalLM(nn.Module):
    # for quark model load
    packed_modules_mapping = {}

    def __init__(
        self,
        config: LongcatFlashConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        # for quark model load
        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
        self.fuse_qkv_a_proj = (
            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
        )
        if self.fuse_qkv_a_proj:
            self.packed_modules_mapping["fused_qkv_a_proj_with_mqa"] = [
                "q_a_proj",
                "kv_a_proj_with_mqa",
            ]

        self.config = config
        self.tp_size = get_tensor_model_parallel_world_size()
        self.quant_config = quant_config
        self.model = LongcatFlashModel(
            config, quant_config, prefix=add_prefix("model", prefix)
        )
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=add_prefix("lm_head", prefix),
            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
        )
        self.logits_processor = LogitsProcessor(config)

    def get_input_embeddings(self) -> nn.Embedding:
        return self.model.embed_tokens

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)

        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch
        )

    def post_load_weights(self, weight_names=None):

        # Perform post-processing after loading weights
        if weight_names is None:
            layer_ids = range(self.config.num_hidden_layers)
        else:
            layer_ids = set()
            for name in weight_names:
                if "kv_b_proj" in name:
                    layer_id = int(name.split(".")[2])
                    if layer_id < self.config.num_hidden_layers:
                        layer_ids.add(layer_id)

        for layer_id in layer_ids:
            for i in range(2):
                self_attn = self.model.layers[layer_id].self_attn[i]
                if hasattr(self_attn.kv_b_proj, "qweight"):
                    # AWQ compatible
                    if _is_cuda or _is_hip:
                        w = awq_dequantize(
                            self_attn.kv_b_proj.qweight,
                            self_attn.kv_b_proj.scales,
                            self_attn.kv_b_proj.qzeros,
                        ).T
                    else:
                        w = awq_dequantize(
                            self_attn.kv_b_proj.qweight,
                            self_attn.kv_b_proj.scales,
                            self_attn.kv_b_proj.qzeros,
                            0,
                            0,
                            0,
                        ).T
                else:
                    w = self_attn.kv_b_proj.weight
                use_deep_gemm_bmm = False

                if w.dtype in (
                    torch.float8_e4m3fn,
                    torch.float8_e4m3fnuz,
                ):
                    if (
                        hasattr(self.quant_config, "weight_block_size")
                        and self.quant_config.weight_block_size is not None
                    ):
                        weight_block_size = self.quant_config.weight_block_size
                        assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
                        if _is_fp8_fnuz:
                            weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                                weight=w,
                                weight_scale=self_attn.kv_b_proj.weight_scale_inv,
                                input_scale=None,
                            )
                        else:
                            weight = w
                            weight_scale = self_attn.kv_b_proj.weight_scale_inv

                        if (
                            _is_cuda
                            and weight_block_size[0] == 128
                            and weight_block_size[1] == 128
                        ):
                            if (
                                deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
                                and not deep_gemm_wrapper.DEEPGEMM_BLACKWELL
                                and get_bool_env_var("SGL_USE_DEEPGEMM_BMM", "false")
                            ):
                                block_scale = weight_scale
                                use_deep_gemm_bmm = True
                            else:
                                w = block_quant_dequant(
                                    weight,
                                    weight_scale,
                                    weight_block_size,
                                    torch.bfloat16,
                                )
                        else:
                            w, scale = block_quant_to_tensor_quant(
                                weight, weight_scale, weight_block_size
                            )
                            self_attn.w_scale = scale
                    else:
                        if _is_fp8_fnuz:
                            weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                                weight=w,
                                weight_scale=self_attn.kv_b_proj.weight_scale,
                                input_scale=None,
                            )
                        else:
                            weight = w
                            weight_scale = self_attn.kv_b_proj.weight_scale

                        w, scale = channel_quant_to_tensor_quant(weight, weight_scale)
                        self_attn.w_scale = scale

                if w.dtype == torch.int8:
                    if hasattr(self.quant_config, "weight_block_size"):
                        # block-wise int8 need it
                        weight_block_size = self.quant_config.weight_block_size
                        if weight_block_size is not None:
                            assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
                            weight = w
                            weight_scale = self_attn.kv_b_proj.weight_scale_inv
                            w = int8_block_dequant(
                                weight, weight_scale, weight_block_size
                            ).to(torch.bfloat16)
                    else:
                        # channel-wise int8 need it
                        w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to(
                            torch.bfloat16
                        )

                w_kc, w_vc = w.unflatten(
                    0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
                ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
                if not use_deep_gemm_bmm:
                    self_attn.w_kc = bind_or_assign(
                        self_attn.w_kc,
                        w_kc.transpose(1, 2).contiguous().transpose(1, 2),
                    )
                    self_attn.w_vc = bind_or_assign(
                        self_attn.w_vc, w_vc.contiguous().transpose(1, 2)
                    )
                    if (
                        hasattr(self_attn.kv_b_proj, "weight_scale")
                        and self_attn.w_scale is None
                    ):
                        self_attn.w_scale = bind_or_assign(
                            self_attn.w_scale, self_attn.kv_b_proj.weight_scale
                        )
                        if _is_hip:
                            self_attn.w_scale *= 2.0
                    # TODO: remove this after adding FP8 support in bmm cpu kernel
                    if (
                        _is_cpu
                        and _is_cpu_amx_available
                        and w.dtype == torch.float8_e4m3fn
                    ):
                        self_attn.w_kc = (
                            self_attn.w_kc.to(torch.bfloat16) * self_attn.w_scale
                        )
                        self_attn.w_vc = (
                            self_attn.w_vc.to(torch.bfloat16) * self_attn.w_scale
                        )
                else:
                    num_tiles_k = self_attn.qk_nope_head_dim // weight_block_size[1]
                    num_tiles_n = self_attn.v_head_dim // weight_block_size[0]
                    ws_kc, ws_vc = block_scale.unflatten(
                        0, (-1, (num_tiles_k + num_tiles_n))
                    ).split([num_tiles_k, num_tiles_n], dim=1)
                    self_attn.w_scale_k = bind_or_assign(
                        self_attn.w_scale_k, ws_kc.transpose(1, 2).contiguous()
                    )
                    self_attn.w_scale_v = bind_or_assign(
                        self_attn.w_scale_v, ws_vc.contiguous()
                    )
                    self_attn.w_kc = bind_or_assign(
                        self_attn.w_kc, w_kc.transpose(1, 2).contiguous()
                    )
                    self_attn.w_vc = bind_or_assign(self_attn.w_vc, w_vc.contiguous())
                    self_attn.use_deep_gemm_bmm = True

                if self.config.mla_scale_q_lora:
                    self_attn.q_a_layernorm.weight.data *= (
                        self.config.hidden_size / self.config.q_lora_rank
                    ) ** 0.5
                if self.config.mla_scale_kv_lora:
                    self_attn.kv_a_layernorm.weight.data *= (
                        self.config.hidden_size / self.config.kv_lora_rank
                    ) ** 0.5

        # TODO(linguoyuan) EPMoE not support DEEPGEMM_BLACKWELL, DeepEP needs to be supported in the future
        deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0 = False

        if (
            deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
            and deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
            and hasattr(self.quant_config, "weight_block_size")
            and self.quant_config.weight_block_size is not None
        ):
            self._weight_requant_ue8m0()

    def _weight_requant_ue8m0(self):
        weight_block_size = self.quant_config.weight_block_size

        for layer_id in range(self.config.num_hidden_layers):
            layer = self.model.layers[layer_id]
            for i in range(2):
                self_attn = layer.self_attn[i]
                module_list = [
                    self_attn.kv_b_proj,
                    self_attn.o_proj,
                ]

                if self.config.q_lora_rank is not None:
                    module_list.append(self_attn.fused_qkv_a_proj_with_mqa)
                    module_list.append(self_attn.q_b_proj)
                else:
                    module_list.append(self_attn.kv_a_proj_with_mqa)
                    module_list.append(self_attn.q_proj)

                for module in module_list:
                    if hasattr(module, "weight_scale_inv"):
                        requant_weight_ue8m0_inplace(
                            module.weight, module.weight_scale_inv, weight_block_size
                        )

                mlp = layer.mlps[i]
                assert isinstance(mlp, LongcatFlashMLP)
                for module in [
                    mlp.gate_up_proj,
                    mlp.down_proj,
                ]:
                    if hasattr(module, "weight_scale_inv"):
                        requant_weight_ue8m0_inplace(
                            module.weight, module.weight_scale_inv, weight_block_size
                        )

        for layer_id in range(self.config.num_hidden_layers):
            experts = layer.mlp.experts
            if isinstance(experts, DeepEPMoE):
                for w in [
                    experts.w13_weight_fp8,
                    experts.w2_weight_fp8,
                ]:
                    requant_weight_ue8m0_inplace(w[0], w[1], weight_block_size)

    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 = get_moe_impl_class().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,
        )

        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
        fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
            self.config.q_lora_rank is not None
        )
        cached_a_proj = {} if fuse_qkv_a_proj else None

        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = []
            params_dict = dict(self.named_parameters())
            weight_names = []
            for name, loaded_weight in weights:
                if "mtp" in name:
                    continue
                weight_names.append(name)
                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
                    futures.append(
                        executor.submit(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
                        futures.append(
                            executor.submit(
                                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
                        if fuse_qkv_a_proj and (
                            "q_a_proj" in name or "kv_a_proj_with_mqa" in name
                        ):
                            cached_a_proj[name] = loaded_weight
                            q_a_proj_name = (
                                name
                                if "q_a_proj" in name
                                else name.replace("kv_a_proj_with_mqa", "q_a_proj")
                            )
                            kv_a_proj_name = (
                                name
                                if "kv_a_proj_with_mqa" in name
                                else name.replace("q_a_proj", "kv_a_proj_with_mqa")
                            )

                            # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
                            if (
                                q_a_proj_name in cached_a_proj
                                and kv_a_proj_name in cached_a_proj
                            ):
                                q_a_proj_weight = cached_a_proj[q_a_proj_name]
                                kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
                                cat_dim = 0
                                if self.quant_config is not None and (
                                    self.quant_config.get_name() == "awq"
                                    or self.quant_config.get_name() == "awq_marlin"
                                    or self.quant_config.get_name() == "moe_wna16"
                                ):
                                    cat_dim = 1
                                fused_weight = torch.cat(
                                    [q_a_proj_weight, kv_a_proj_weight], dim=cat_dim
                                )
                                param_name = (
                                    name.replace(
                                        "q_a_proj", "fused_qkv_a_proj_with_mqa"
                                    )
                                    if "q_a_proj" in name
                                    else name.replace(
                                        "kv_a_proj_with_mqa",
                                        "fused_qkv_a_proj_with_mqa",
                                    )
                                )
                                param = params_dict[param_name]

                                weight_loader = getattr(
                                    param, "weight_loader", default_weight_loader
                                )
                                futures.append(
                                    executor.submit(weight_loader, param, fused_weight)
                                )
                                cached_a_proj.pop(q_a_proj_name)
                                cached_a_proj.pop(kv_a_proj_name)
                        else:
                            if (
                                "k_scale" in name or "v_scale" in name
                            ) and name not in params_dict:
                                # modelopt attn kv scale is named differently
                                for scale in ["k_scale", "v_scale"]:
                                    if scale in name:
                                        name = name.replace(
                                            f"{scale[0]}_proj", "attn_mqa"
                                        )
                                        break
                            if name not in params_dict:
                                # modelopt ckpt contains not needed weights for MTP module:
                                # model.decoder.self_attn.attn_mqa.v_scale and
                                # model.decoder.self_attn.attn_mqa.k_scale
                                logger.warning(f"{name} not found in params_dict.")
                                continue
                            param = params_dict[name]
                            weight_loader = getattr(
                                param, "weight_loader", default_weight_loader
                            )
                            futures.append(
                                executor.submit(weight_loader, param, loaded_weight)
                            )

            # Wait for all tasks to complete and raise any exceptions.
            for future in concurrent.futures.as_completed(futures):
                future.result()

        self.post_load_weights(weight_names=weight_names)

    def get_embed_and_head(self):
        return self.model.embed_tokens.weight, self.lm_head.weight

    def set_embed_and_head(self, embed, head):
        del self.model.embed_tokens.weight
        del self.lm_head.weight
        self.model.embed_tokens.weight = embed
        self.lm_head.weight = head
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

    @classmethod
    def get_model_config_for_expert_location(cls, config):
        return ModelConfigForExpertLocation(
            num_layers=config.num_hidden_layers,
            num_logical_experts=config.n_routed_experts,
        )


EntryClass = [LongcatFlashForCausalLM]
