# Copyright 2026 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.
# ==============================================================================

import concurrent.futures
import logging
from dataclasses import dataclass
from typing import Dict, Iterable, List, Optional, Tuple

import torch
import torch.nn as nn
import tqdm
from transformers import PretrainedConfig

from sglang.srt.distributed.parallel_state import GroupCoordinator
from sglang.srt.environ import envs
from sglang.srt.layers import deep_gemm_wrapper
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.fp8_utils import (
    block_quant_dequant,
    block_quant_to_tensor_quant,
    channel_quant_to_tensor_quant,
    inverse_transform_scale_ue8m0,
    normalize_e4m3fn_to_e4m3fnuz,
    quant_weight_ue8m0,
)
from sglang.srt.layers.quantization.int8_utils import (
    block_dequant as int8_block_dequant,
)
from sglang.srt.layers.utils import get_layer_id
from sglang.srt.model_loader.utils import (
    maybe_executor_submit,
    should_async_load,
    should_deepgemm_weight_requant_ue8m0,
)
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.deepseek_common.utils import (
    _is_cuda,
    _is_fp8_fnuz,
    _is_hip,
    _is_npu,
    _is_xpu,
    _use_aiter_gfx95,
    awq_dequantize_func,
    enable_nextn_moe_bf16_cast_to_fp8,
)
from sglang.srt.utils import bind_or_assign, get_bool_env_var, log_info_on_rank0

if _use_aiter_gfx95:
    from sglang.srt.layers.quantization.quark.utils import quark_post_load_weights

logger = logging.getLogger(__name__)

# Optional quantization for DeepSeek nvfp4 checkpoint
NVFP4_CKPT_FP8_ATTN_QUANT_MODULES = ["q_b_proj"]


@dataclass(frozen=True)
class NextNEnabledConfig:
    num_nextn_layers: int
    nextn_layer_id: int
    nextn_layer_prefix: str
    nextn_spec_weight_names: List[str]


@dataclass(frozen=True)
class NextNDisabledConfig:
    pass


"""Union type for NextN configuration, including enabled and disabled configurations."""
NextNConfig = NextNEnabledConfig | NextNDisabledConfig


class DeepseekV2WeightLoaderMixin:
    """Mixin for loading weights in DeepSeek V2/V3 models."""

    model: nn.Module
    config: PretrainedConfig
    quant_config: Optional[QuantizationConfig]
    pp_group: GroupCoordinator
    num_fused_shared_experts: int

    def do_load_weights(
        self,
        weights: Iterable[Tuple[str, torch.Tensor]],
        is_nextn: bool = False,
    ):
        """Load model weights from checkpoint.

        Args:
            weights: Iterable of (weight_name, weight_tensor) pairs
            is_nextn: Whether loading NextN speculative decoding weights
        """
        nextn_conf = self._initialize_nextn_conf(is_nextn)

        weights = self._maybe_quant_weights_to_fp8_ue8m0(
            weights, NVFP4_CKPT_FP8_ATTN_QUANT_MODULES, nextn_conf
        )

        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 + self.num_fused_shared_experts,
        )
        # Params for special naming rules in mixed-precision models, for example:
        # model.layers.xx.mlp.experts.xx.w1.input_scale. For details,
        # see https://huggingface.co/Barrrrry/DeepSeek-R1-W4AFP8/blob/main.
        if self.quant_config and self.quant_config.get_name() == "w4afp8":
            expert_params_mapping += FusedMoE.make_expert_input_scale_params_mapping(
                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

        if self.num_fused_shared_experts > 0:
            assert self.num_fused_shared_experts == 1
            log_info_on_rank0(logger, "Shared experts fusion optimization enabled.")

        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = []
            params_dict = dict(self.named_parameters())
            weight_names = []
            for name, loaded_weight in weights:
                use_async_loading = should_async_load(loaded_weight)
                layer_id = get_layer_id(name)
                if (
                    layer_id is not None
                    and hasattr(self.model, "start_layer")
                    and (
                        layer_id < self.model.start_layer
                        or layer_id >= self.model.end_layer
                    )
                ):
                    continue
                if self.num_fused_shared_experts > 0 and "mlp.shared_experts" in name:
                    name = name.replace(
                        "mlp.shared_experts",
                        f"mlp.experts.{self.config.n_routed_experts}",
                    )

                weight_names.append(name)

                match nextn_conf:
                    case NextNEnabledConfig(
                        nextn_layer_prefix=layer_prefix,
                        nextn_spec_weight_names=spec_weight_names,
                    ):
                        if not name.startswith(layer_prefix):
                            continue

                        # Use shared head and embed weights from target model
                        if "shared_head.head" in name or "embed_tokens" in name:
                            continue

                        # Transform name: NextN-specific → "model.*", decoder → "model.decoder.*"
                        if any(s in name for s in spec_weight_names):
                            name = name.replace(layer_prefix, "model")
                        else:
                            name = name.replace(layer_prefix, "model.decoder")
                    case NextNDisabledConfig():
                        if hasattr(self.config, "num_nextn_predict_layers"):
                            num_nextn_layers = self.config.num_nextn_predict_layers
                            if num_nextn_layers > 0 and name.startswith("model.layers"):
                                name_list = name.split(".")
                                if (
                                    len(name_list) >= 3
                                    and int(name_list[2])
                                    >= self.config.num_hidden_layers
                                ):
                                    continue

                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
                    if _is_npu:
                        name = name.replace("weight_packed", "weight")
                    # 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
                    maybe_executor_submit(
                        executor=executor,
                        futures=futures,
                        use_async=use_async_loading,
                        func=weight_loader,
                        func_args=(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
                        if _is_npu:
                            name = name.replace("weight_packed", "weight")
                        name = name.replace(weight_name, param_name)
                        if name not in params_dict:
                            continue
                        param = params_dict[name]
                        weight_loader = param.weight_loader
                        maybe_executor_submit(
                            executor=executor,
                            futures=futures,
                            use_async=use_async_loading,
                            func=weight_loader,
                            func_args=(
                                param,
                                loaded_weight,
                                name,
                            ),
                            func_kwargs={
                                "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
                        # Skip loading embed_tokens if not first rank in pipeline parallelism
                        if ".embed_tokens." in name and not self.pp_group.is_first_rank:
                            continue
                        # Skip loading norm if not last rank in pipeline parallelism
                        if ".norm." in name and not self.pp_group.is_last_rank:
                            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]

                                if q_a_proj_weight.shape == torch.Size(
                                    []
                                ) and kv_a_proj_weight.shape == torch.Size([]):
                                    fused_weight = q_a_proj_weight
                                else:
                                    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
                                )
                                maybe_executor_submit(
                                    executor=executor,
                                    futures=futures,
                                    use_async=use_async_loading,
                                    func=weight_loader,
                                    func_args=(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
                            )
                            maybe_executor_submit(
                                executor=executor,
                                futures=futures,
                                use_async=use_async_loading,
                                func=weight_loader,
                                func_args=(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(is_nextn=is_nextn, weight_names=weight_names)

    def _initialize_nextn_conf(self, is_nextn: bool) -> NextNConfig:
        """
        Initialize the nextn configuration.

        Raises:
            ValueError: If num_nextn_predict_layers is not in the config.
            AssertionError: If num_nextn_predict_layers is not equal to 1.
        """
        if not is_nextn:
            return NextNDisabledConfig()

        if not hasattr(self.config, "num_nextn_predict_layers"):
            raise ValueError("num_nextn_predict_layers is not in the config")

        num_nextn_layers = self.config.num_nextn_predict_layers
        assert num_nextn_layers == 1, "Only 1 nextn layer is supported"

        # compatible with old design
        nextn_layer_id = (
            0 if self.config.num_hidden_layers == 1 else self.config.num_hidden_layers
        )

        return NextNEnabledConfig(
            num_nextn_layers=num_nextn_layers,
            nextn_layer_id=nextn_layer_id,
            nextn_layer_prefix=f"model.layers.{nextn_layer_id}",
            nextn_spec_weight_names=[
                "shared_head.norm",
                "eh_proj",
                "enorm",
                "hnorm",
            ],
        )

    def post_load_weights(
        self,
        is_nextn: bool = False,
        weight_names: Optional[Iterable[str]] = None,
    ) -> None:
        """Post-process weights after loading.

        Handles kv_b_proj weight processing including:
        - AWQ dequantization
        - FP8/INT8 requantization and block-wise to tensor-wise conversion
        - Splitting weights into w_kc and w_vc components for MLA

        Args:
            is_nextn: Whether processing NextN weights
            weight_names: Optional list of loaded weight names to determine which layers to process
        """
        if is_nextn:
            layer_ids = [self.config.num_hidden_layers]
        else:
            if weight_names is None:
                layer_ids = range(self.model.start_layer, self.model.end_layer)
            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:
            self_attn = (
                self.model.layers[layer_id].self_attn
                if not is_nextn
                else self.model.decoder.self_attn
            )

            if hasattr(self_attn.kv_b_proj, "qweight"):
                # awq compatible, dequantize the weight if supported
                awq_dequantize_f = awq_dequantize_func()
                if awq_dequantize_f is not None:
                    w = awq_dequantize_f(
                        self_attn.kv_b_proj.qweight,
                        self_attn.kv_b_proj.scales,
                        self_attn.kv_b_proj.qzeros,
                    ).T
                else:
                    raise ValueError(
                        "AWQ dequantize function is not supported for the current device"
                    )
            else:
                w = self_attn.kv_b_proj.weight

            # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
            # This may affect the accuracy of fp8 model.
            # Fix deepseek v3 blockwise bmm by using deep_gemm
            use_deep_gemm_bmm = False

            if w.dtype in (
                torch.float8_e4m3fn,
                torch.float8_e4m3fnuz,
            ):
                # For mixed quantization (experts int4, linear fp8), use linear_fp8_config
                selected_quant_config = getattr(
                    self.quant_config, "linear_fp8_config", None
                )
                if selected_quant_config is None:
                    selected_quant_config = self.quant_config
                weight_block_size = getattr(
                    selected_quant_config, "weight_block_size", None
                )
                if weight_block_size is not None:
                    assert hasattr(self_attn.kv_b_proj, "weight_scale_inv") or hasattr(
                        self_attn.kv_b_proj, "weight_scale"
                    )
                    weight_scale = (
                        self_attn.kv_b_proj.weight_scale
                        if hasattr(self_attn.kv_b_proj, "weight_scale")
                        else self_attn.kv_b_proj.weight_scale_inv
                    )
                    if _is_fp8_fnuz:
                        weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                            weight=w,
                            weight_scale=weight_scale,
                            input_scale=None,
                        )
                    else:
                        weight = w

                    # In multiple weight loading scenarios (e.g. RL), we need to inverse the scale of the weights after the requantization happened at the first loading.
                    if (
                        should_deepgemm_weight_requant_ue8m0(
                            weight_block_size=getattr(
                                self.quant_config, "weight_block_size", None
                            )
                        )
                        and weight_scale.format_ue8m0
                    ):
                        weight_scale = inverse_transform_scale_ue8m0(
                            weight_scale, mn=weight.shape[-2]
                        )

                    if (
                        (_is_cuda or _is_xpu)
                        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 (
                _use_aiter_gfx95
                and self.quant_config is not None
                and self.quant_config.get_name() == "quark"
            ):
                w_kc, self_attn.w_scale_k, w_vc, self_attn.w_scale_v = (
                    quark_post_load_weights(self_attn, w, "mxfp4")
                )

            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)
                )
                w_vc = w_vc.contiguous().transpose(1, 2)
                if _is_npu:
                    w_vc = w_vc.contiguous()
                self_attn.w_vc = bind_or_assign(self_attn.w_vc, w_vc)
                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
            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

    @classmethod
    def generate_weight_name_filter(cls, logical_experts_map: Dict[int, List[int]]):
        """
        Generates a filter function that tests whether the (layer_id, expert_id)
        indicated by a param name lies in the `logical_experts` map
        Args:
            logical_experts_map: a map of layer_id to expert_ids, specifying a list of expert_ids by a specific layer_id.

        Returns:
            A function (name: str) -> bool
        """
        import re

        # Regex pattern to extract layer_id and expert_id from weight name
        pattern = re.compile(r"layers\.(\d+)\.mlp\.experts\.(\d+)\.")

        def weight_name_filter(name: str) -> bool:
            match = pattern.search(name)
            if match:
                layer_id, expert = int(match.group(1)), int(match.group(2))
                # First check if layer_id exists, then check if expert is in the list
                return (
                    layer_id in logical_experts_map
                    and expert in logical_experts_map[layer_id]
                )
            return False

        return weight_name_filter

    def _maybe_quant_weights_to_fp8_ue8m0(
        self,
        weights,
        attn_quant_modules,
        nextn_conf: NextNConfig,
    ):
        """Optionally quantize weights to FP8 UE8M0 format for DeepSeek nvfp4 checkpoints.

        Args:
            weights: Iterable of (name, tensor) weight pairs
            attn_quant_modules: List of attention module names to quantize
            nextn_conf: NextN configuration

        Returns:
            Original weights iterator if no quantization needed,
            otherwise list of (name, tensor) pairs with quantized weights
        """
        weight_block_size = [128, 128]
        partial_names = []

        match nextn_conf:
            case NextNEnabledConfig(nextn_layer_id=layer_id):
                if envs.SGLANG_NVFP4_CKPT_FP8_GEMM_IN_ATTN.get():
                    for stem in attn_quant_modules:
                        partial_names.append(
                            f"model.layers.{layer_id}.self_attn.{stem}"
                        )

                if enable_nextn_moe_bf16_cast_to_fp8(self.quant_config):
                    expert_sub_names = ["shared_experts"] + [
                        f"experts.{i}" for i in range(self.config.n_routed_experts)
                    ]
                    for expert_sub_name in expert_sub_names:
                        for stem in ["gate_proj", "up_proj", "down_proj"]:
                            partial_names.append(
                                f"model.layers.{layer_id}.mlp.{expert_sub_name}.{stem}"
                            )

            case NextNDisabledConfig():
                if envs.SGLANG_NVFP4_CKPT_FP8_GEMM_IN_ATTN.get():
                    for layer_id in range(self.config.num_hidden_layers):
                        for stem in attn_quant_modules:
                            partial_names.append(
                                f"model.layers.{layer_id}.self_attn.{stem}"
                            )

        # Early return if no quantization needed - avoid materializing all weights into memory
        if not partial_names:
            return weights

        # Only materialize weights dict when quantization is actually needed
        weights_dict = dict(weights)

        for partial_name in tqdm.tqdm(partial_names, desc="quant weights to fp8 ue8m0"):
            original_weight = weights_dict[f"{partial_name}.weight"]
            out_w, out_s = quant_weight_ue8m0(
                original_weight, weight_block_size=weight_block_size
            )
            weights_dict[f"{partial_name}.weight"] = out_w
            weights_dict[f"{partial_name}.weight_scale_inv"] = out_s

        if isinstance(
            nextn_conf, NextNEnabledConfig
        ) and enable_nextn_moe_bf16_cast_to_fp8(self.quant_config):
            self._mark_nextn_moe_weights_as_ue8m0()

        return list(weights_dict.items())

    def _mark_nextn_moe_weights_as_ue8m0(self):
        """Mark NextN MoE weight scales as UE8M0 format to avoid requantization."""
        experts = self.model.decoder.mlp.experts
        w13_scale = (
            experts.w13_weight_scale_inv
            if hasattr(experts, "w13_weight_scale_inv")
            else experts.w13_weight_scale
        )
        w2_scale = (
            experts.w2_weight_scale_inv
            if hasattr(experts, "w2_weight_scale_inv")
            else experts.w2_weight_scale
        )
        w13_scale.format_ue8m0 = True
        w2_scale.format_ue8m0 = True
