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

from typing import Callable, Optional

import torch
from compressed_tensors.quantization import (QuantizationArgs,
                                             QuantizationStrategy)
from torch.nn import Parameter

from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
    CompressedTensorsScheme)
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    apply_fp8_block_linear, check_aiter_fp8_linear_support,
    create_fp8_input_scale, create_fp8_scale_parameter,
    create_fp8_weight_parameter, maybe_post_process_fp8_weight_block,
    process_fp8_weight_block_strategy, process_fp8_weight_channel_strategy,
    process_fp8_weight_tensor_strategy, validate_fp8_block_shape)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    Fp8LinearOp, cutlass_block_fp8_supported, maybe_create_device_identity)
from vllm.model_executor.parameter import (BlockQuantScaleParameter,
                                           ChannelQuantScaleParameter,
                                           PerTensorScaleParameter)

__all__ = ["CompressedTensorsW8A8Fp8"]

strategy_to_parameter_type = {
    QuantizationStrategy.BLOCK: BlockQuantScaleParameter,
    QuantizationStrategy.CHANNEL: ChannelQuantScaleParameter,
    QuantizationStrategy.TENSOR: PerTensorScaleParameter,
}


class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):

    def __init__(self, weight_quant: QuantizationArgs,
                 is_static_input_scheme: bool):
        self.weight_quant = weight_quant
        self.strategy = weight_quant.strategy
        self.out_dtype = torch.get_default_dtype()
        self.is_static_input_scheme = is_static_input_scheme
        self.act_q_group_shape = GroupShape.PER_TENSOR \
            if is_static_input_scheme else GroupShape.PER_TOKEN
        self.fp8_linear = Fp8LinearOp(
            act_quant_static=self.is_static_input_scheme,
            act_quant_group_shape=self.act_q_group_shape)

        self.weight_block_size = self.weight_quant.block_structure
        self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
        self.use_aiter_and_is_supported = check_aiter_fp8_linear_support()

    @classmethod
    def get_min_capability(cls) -> int:
        # lovelace and up
        return 89

    def create_weights(self, layer: torch.nn.Module,
                       input_size_per_partition: int,
                       output_partition_sizes: list[int], input_size: int,
                       output_size: int, params_dtype: torch.dtype,
                       weight_loader: Callable, **kwargs):
        maybe_create_device_identity()

        output_size_per_partition = sum(output_partition_sizes)
        layer.logical_widths = output_partition_sizes
        layer.weight_block_size = None

        if self.strategy == QuantizationStrategy.BLOCK:
            assert self.weight_block_size is not None
            layer.weight_block_size = self.weight_block_size
            # Validate block quantization shapes
            validate_fp8_block_shape(layer, input_size, output_size,
                                     input_size_per_partition,
                                     output_partition_sizes,
                                     self.weight_block_size)

        # WEIGHT
        weight = create_fp8_weight_parameter(output_size_per_partition,
                                             input_size_per_partition,
                                             weight_loader)
        layer.register_parameter("weight", weight)

        # WEIGHT SCALE
        weight_scale = create_fp8_scale_parameter(
            strategy_to_parameter_type[self.strategy], output_partition_sizes,
            input_size_per_partition, layer.weight_block_size, weight_loader)
        layer.register_parameter("weight_scale", weight_scale)

        # INPUT SCALE
        if self.is_static_input_scheme:
            input_scale = create_fp8_input_scale(output_partition_sizes,
                                                 weight_loader)
            layer.register_parameter("input_scale", input_scale)

    def process_weights_after_loading(self, layer) -> None:
        if self.strategy == QuantizationStrategy.TENSOR:
            weight, weight_scale, input_scale = (
                process_fp8_weight_tensor_strategy(
                    layer.weight, layer.weight_scale, layer.logical_widths,
                    getattr(layer, 'input_scale', None)))
            weight = weight.t()

        elif self.strategy == QuantizationStrategy.CHANNEL:
            weight, weight_scale, input_scale = (
                process_fp8_weight_channel_strategy(
                    layer.weight, layer.weight_scale,
                    getattr(layer, 'input_scale', None)))
            weight = weight.t()

        elif self.strategy == QuantizationStrategy.BLOCK:
            assert self.is_static_input_scheme is False
            weight, weight_scale = process_fp8_weight_block_strategy(
                layer.weight, layer.weight_scale)
            input_scale = None

        else:
            raise ValueError(f"Unknown quantization strategy {self.strategy}")

        # required by torch.compile to be torch.nn.Parameter
        layer.weight = Parameter(weight.data, requires_grad=False)
        layer.weight_scale = Parameter(weight_scale.data, requires_grad=False)
        if input_scale is not None:
            layer.input_scale = Parameter(input_scale.data,
                                          requires_grad=False)

        # INPUT SCALE
        if self.is_static_input_scheme and hasattr(layer, 'input_scale'):
            layer.input_scale = Parameter(layer.input_scale.max(),
                                          requires_grad=False)
        else:
            layer.input_scale = None

        if self.strategy == QuantizationStrategy.BLOCK:
            maybe_post_process_fp8_weight_block(
                layer, self.cutlass_block_fp8_supported)

    def apply_weights(self,
                      layer: torch.nn.Module,
                      x: torch.Tensor,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:

        if layer.weight_block_size is not None:
            return apply_fp8_block_linear(
                layer,
                input=x,
                bias=bias,
                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
                use_aiter_and_is_supported=self.use_aiter_and_is_supported)

        return self.fp8_linear.apply(input=x,
                                     weight=layer.weight,
                                     weight_scale=layer.weight_scale,
                                     out_dtype=self.out_dtype,
                                     input_scale=layer.input_scale,
                                     bias=bias)
