# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import torch

import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.model_executor.layers.fused_moe import (
    FusedMoeWeightScaleSupported,
    RoutedExperts,
    SharedExperts,
)
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig,
    FusedMoEQuantConfig,
)
from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
    convert_to_fp8_moe_kernel_format,
    make_fp8_moe_kernel,
    make_fp8_moe_quant_config,
)
from vllm.model_executor.layers.fused_moe.oracle.mxfp8 import (
    select_mxfp8_moe_backend,
)
from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe import (  # noqa: E501
    CompressedTensorsMoEMethod,
)
from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
    MXFP8_BLOCK_SIZE,
    MXFP8_SCALE_DTYPE,
    MXFP8_VALUE_DTYPE,
)
from vllm.model_executor.utils import replace_parameter, set_weight_attrs


class CompressedTensorsW8A8Mxfp8MoEMethod(CompressedTensorsMoEMethod):
    """Compressed-tensors MoE method for pre-quantized MXFP8 (W8A8) checkpoints.

    Loads FP8 (E4M3) weights with E8M0 uint8 per-group scales (group_size=32)
    from checkpoint. Activations are dynamically quantized to MXFP8 at runtime.
    Supports FlashInfer TRT-LLM and Marlin backends (auto-selected).
    """

    def __init__(self, moe: FusedMoEConfig):
        super().__init__(moe)
        self.weight_block_size = [1, MXFP8_BLOCK_SIZE]
        self.fp8_backend, self.experts_cls = select_mxfp8_moe_backend(config=self.moe)

    def create_weights(
        self,
        layer: torch.nn.Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        layer.num_experts = num_experts
        layer.params_dtype = params_dtype
        w13_num_shards = 2 if self.moe.is_act_and_mul else 1

        w13_weight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                hidden_size,
                dtype=MXFP8_VALUE_DTYPE,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight", w13_weight)
        set_weight_attrs(w13_weight, extra_weight_attrs)

        w2_weight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition,
                dtype=MXFP8_VALUE_DTYPE,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        w13_weight_scale = torch.nn.Parameter(
            torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                hidden_size // MXFP8_BLOCK_SIZE,
                dtype=MXFP8_SCALE_DTYPE,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.GROUP.value}
        )
        set_weight_attrs(w13_weight_scale, extra_weight_attrs)

        w2_weight_scale = torch.nn.Parameter(
            torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition // MXFP8_BLOCK_SIZE,
                dtype=MXFP8_SCALE_DTYPE,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight_scale", w2_weight_scale)
        set_weight_attrs(w2_weight_scale, extra_weight_attrs)

        layer.w13_input_scale = None
        layer.w2_input_scale = None

    def process_weights_after_loading(self, layer: RoutedExperts) -> None:
        layer.weight_block_size = self.weight_block_size

        w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
            fp8_backend=self.fp8_backend,
            layer=layer,
            w13=layer.w13_weight,
            w2=layer.w2_weight,
            w13_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            w13_input_scale=layer.w13_input_scale,
            w2_input_scale=layer.w2_input_scale,
        )

        replace_parameter(layer, "w13_weight", w13)
        replace_parameter(layer, "w2_weight", w2)
        replace_parameter(layer, "w13_weight_scale", w13_scale)
        replace_parameter(layer, "w2_weight_scale", w2_scale)

        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        if self.moe_quant_config is not None:
            assert self.experts_cls is not None
            self.moe_kernel = make_fp8_moe_kernel(
                moe_quant_config=self.moe_quant_config,
                moe_config=self.moe,
                fp8_backend=self.fp8_backend,
                experts_cls=self.experts_cls,
                routing_tables=layer._expert_routing_tables(),
            )

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        return make_fp8_moe_quant_config(
            fp8_backend=self.fp8_backend,
            w1_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            a1_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
            block_shape=self.weight_block_size,
            swiglu_limit=getattr(layer, "swiglu_limit", None),
        )

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel "
            "initialization logic. This function should not be called."
        )

    def apply_monolithic(
        self,
        layer: RoutedExperts,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        input_ids: torch.Tensor | None = None,
    ) -> torch.Tensor:
        assert self.moe_kernel is not None
        return self.moe_kernel.apply_monolithic(
            x,
            layer.w13_weight,
            layer.w2_weight,
            router_logits,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            num_expert_group=layer.num_expert_group,
            topk_group=layer.topk_group,
            e_score_correction_bias=layer.e_score_correction_bias,
            routed_scaling_factor=layer.routed_scaling_factor,
        )

    def apply(
        self,
        layer: RoutedExperts,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts: SharedExperts | None,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor:
        assert not self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights,
            topk_ids,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            shared_experts=shared_experts,
            shared_experts_input=shared_experts_input,
        )
