################################################################################
# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
# 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 torch
from torch import nn

from vllm.attention import Attention
from vllm.config import ModelConfig
from vllm.model_executor.layers.linear import QKVCrossParallelLinear
from vllm.model_executor.layers.quantization.base_config import (
    QuantizeMethodBase)


def process_weights_after_loading(model: nn.Module, model_config: ModelConfig,
                                  target_device: torch.device) -> None:
    for _, module in model.named_modules():
        if isinstance(module, QKVCrossParallelLinear):
            # NOTE(Isotr0py): special case for cross QKV layer because
            # q and kv proj aren't registered as submodules intentionally
            module.process_weights_after_loading()
            torch.supa.empty_cache()

            continue
        quant_method = getattr(module, "quant_method", None)
        if isinstance(quant_method, QuantizeMethodBase):
            # When quant methods need to process weights after loading
            # (for repacking, quantizing, etc), they expect parameters
            # to be on the global target device. This scope is for the
            # case where cpu offloading is used, where we will move the
            # parameters onto device for processing and back off after.

            # with device_loading_context(module, target_device):
            quant_method.weight_type = model_config.weight_type
            quant_method.use_ds_mla = model_config.use_ds_mla
            quant_method.use_ds_mla_sparse = model_config.use_ds_mla_sparse
            quant_method.process_weights_after_loading(module)

            torch.supa.empty_cache()

    # Currently only used by MLA.
    # NOTE: This intentionally happens after other modules so we can easily
    # decompress the weights for MLA.
    for _, module in model.named_modules():
        if isinstance(module, Attention) and \
            hasattr(module, "process_weights_after_loading"):
            # TODO(lucas): see if there is a way to unify the signatures
            # of process_weights_after_loading
            module.process_weights_after_loading(model_config.dtype)
            torch.supa.empty_cache()