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

from typing import Any, Optional

import torch
from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE

from vllm._custom_ops import (
    cpu_gemm_wna16,
)
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import (
    LinearBase,
    LinearMethodBase,
    UnquantizedLinearMethod,
)
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig,
    QuantizeMethodBase,
)
from vllm.model_executor.layers.quantization.utils.gptq_utils import (
    get_linear_quant_method,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    marlin_repeat_scales_on_all_ranks,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    is_layer_skipped,
    pack_cols,
    unpack_cols,
)
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.models.utils import WeightsMapper
from vllm.model_executor.parameter import (
    ChannelQuantScaleParameter,
    GroupQuantScaleParameter,
    PackedColumnParameter,
    PackedvLLMParameter,
    RowvLLMParameter,
)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.transformers_utils.config import get_safetensors_params_metadata
from vllm.utils.collection_utils import is_list_of

logger = init_logger(__name__)


class CPUGPTQConfig(QuantizationConfig):
    """Config class for CPU GPTQ quant"""

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        desc_act: bool,
        is_sym: bool,
        lm_head_quantized: bool,
        dynamic: dict[str, dict[str, int | bool]],
        full_config: dict[str, Any],
        modules_in_block_to_quantize: list[str] | None = None,
    ) -> None:
        super().__init__()
        if desc_act and group_size == -1:
            # In this case, act_order == True is the same as act_order == False
            # (since we have only one group per output channel)
            desc_act = False

        # GPTQModel use `dynamic` config property to allow per module
        # quantization config so each module can be individually optimized.
        # Format is dict[str, dict] where key is a regex string that can
        # perform both positive ("+:" prefixed) or negative ("-:" prefixed)
        # matching of a module.
        # Default to positive match, override base quant config mode, if no
        # prefix is used. Value is in dict format of field key and override
        # value.
        # Negative matching will skip quantization init for this module
        # entirely:
        # non-quantized inference. More details and quantization examples can be
        # found at: https://github.com/ModelCloud/GPTQModel
        # Example:
        #  # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
        #  # last 1/4 of the layers 16-21 has 8bit and group_size 64
        # dynamic = {
        #  #`.*\.` matches the layers_node prefix
        #  # positive match layer 10-15
        #  r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
        #  # positive match layer 16-21
        #  r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
        #  r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
        # }
        assert weight_bits == 4
        self.dynamic = dynamic
        self.weight_bits = weight_bits
        self.is_sym = is_sym
        self.pack_factor = 32 // weight_bits  # packed into int32
        self.group_size = group_size
        self.desc_act = desc_act
        self.lm_head_quantized = lm_head_quantized
        self.full_config = full_config
        self.modules_in_block_to_quantize = modules_in_block_to_quantize or []

    def __repr__(self) -> str:
        return (
            f"CPUWNA16Config("
            f"group_size={self.group_size}, "
            f"desc_act={self.desc_act}, "
            f"lm_head_quantized={self.lm_head_quantized}, "
            f"dynamic={self.dynamic}, "
            f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})"
        )

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "cpu_gptq"

    @classmethod
    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
        return [torch.half, torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        return -1

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return ["quantize_config.json"]

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "CPUGPTQConfig":
        weight_bits = cls.get_from_keys(config, ["bits"])
        desc_act = cls.get_from_keys_or(config, ["desc_act"], default=False)
        dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
        group_size = cls.get_from_keys(config, ["group_size"])
        is_sym = cls.get_from_keys(config, ["sym"])
        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
        modules_in_block_to_quantize = cls.get_from_keys_or(
            config, ["modules_in_block_to_quantize"], default=None
        )
        return cls(
            weight_bits,
            group_size,
            desc_act,
            is_sym,
            lm_head_quantized,
            dynamic,
            config,
            modules_in_block_to_quantize,
        )

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        quant_method = hf_quant_cfg.get("quant_method", "").lower()
        if current_platform.is_cpu() and (quant_method == "gptq"):
            return cls.get_name()
        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        return get_linear_quant_method(self, layer, prefix, CPUGPTQLinearMethod)  # type: ignore

    def apply_vllm_mapper(self, hf_to_vllm_mapper):
        if self.modules_in_block_to_quantize is not None:
            self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list(
                self.modules_in_block_to_quantize
            )

    def maybe_update_config(self, model_name: str, revision: str | None = None):
        if self.modules_in_block_to_quantize:
            if is_list_of(self.modules_in_block_to_quantize, list):
                # original modules_in_block_to_quantize: list[list[str]]
                # flatten original modules_in_block_to_quantize
                self.modules_in_block_to_quantize = [
                    item
                    for sublist in self.modules_in_block_to_quantize
                    for item in sublist
                ]
            return

        unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
        metadata = get_safetensors_params_metadata(model_name, revision=revision)
        quant_layers: set[str] = {
            param_name.rsplit(".", 1)[0]
            for param_name, info in metadata.items()
            if (dtype := info.get("dtype", None))
            and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
        }
        self.modules_in_block_to_quantize = list(quant_layers)


class CPUGPTQLinearMethod(LinearMethodBase):
    """Linear method for GPTQ on CPU.

    Args:
        quant_config: The CPUWNA16 quantization config.
    """

    def __init__(self, quant_config: CPUGPTQConfig) -> None:
        self.quant_config = quant_config
        assert self.quant_config.is_sym, "GPTQ asym quant is not supported on CPU"

    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,
        **extra_weight_attrs,
    ) -> None:
        output_size_per_partition = sum(output_partition_sizes)
        assert output_size_per_partition * self.quant_config.weight_bits % 32 == 0
        assert output_size_per_partition % 32 == 0
        assert input_size_per_partition % 32 == 0

        is_row_parallel = input_size != input_size_per_partition
        weight_loader = extra_weight_attrs.get("weight_loader")

        # Normalize group_size
        if self.quant_config.group_size != -1:
            group_size = self.quant_config.group_size
        else:
            group_size = input_size

        # Determine sharding
        if marlin_repeat_scales_on_all_ranks(
            self.quant_config.desc_act, self.quant_config.group_size, is_row_parallel
        ):
            # By setting scale_dim == None, weight_loader will
            # repeat the scales on each rank in TP>1 case.
            scales_and_zp_input_dim = None
            scales_and_zp_size = input_size // group_size
        else:
            # By setting scale_dim == 0, weight_loader will
            # shard the scales in TP>1 case.
            scales_and_zp_input_dim = 0
            scales_and_zp_size = input_size_per_partition // group_size

        # Quantized weights
        qweight = PackedvLLMParameter(
            data=torch.empty(
                input_size_per_partition // self.quant_config.pack_factor,
                output_size_per_partition,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=0,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        # Activation order
        g_idx = RowvLLMParameter(
            data=torch.empty(
                input_size_per_partition,
                dtype=torch.int32,
            ),
            input_dim=0,
            weight_loader=weight_loader,
        )
        set_weight_attrs(
            g_idx,
            {"ignore_warning": True},
        )

        qzeros_args = {
            "data": torch.empty(
                scales_and_zp_size,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            "weight_loader": weight_loader,
        }
        weight_scale_args = {
            "data": torch.empty(
                scales_and_zp_size,
                output_size_per_partition,
                dtype=params_dtype,
            ),
            "weight_loader": weight_loader,
        }

        if scales_and_zp_input_dim is None:
            scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args)
            qzeros = PackedColumnParameter(
                output_dim=1,
                packed_dim=1,
                packed_factor=self.quant_config.pack_factor,
                **qzeros_args,
            )

        else:
            scales = GroupQuantScaleParameter(
                output_dim=1, input_dim=0, **weight_scale_args
            )
            qzeros = PackedvLLMParameter(
                input_dim=0,
                output_dim=1,
                packed_dim=1,
                packed_factor=self.quant_config.pack_factor,
                **qzeros_args,
            )

        layer.register_parameter("qweight", qweight)
        layer.register_parameter("g_idx", g_idx)
        layer.register_parameter("scales", scales)
        layer.register_parameter("qzeros", qzeros)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        torch.set_printoptions(profile="full", linewidth=5000, sci_mode=False)
        packed_weight = layer.qweight.data
        bits = self.quant_config.weight_bits
        pack_factor = int(self.quant_config.pack_factor)
        p_w_k, p_w_n = packed_weight.size()
        input_size = p_w_k * pack_factor
        output_size = p_w_n
        isa_hint = _get_isa_hint(layer.scales.dtype)
        layer.isa_hint = isa_hint

        layer.qzeros = None
        if not self.quant_config.desc_act:
            layer.g_idx = None

        # convert input dim packed to output dim packed
        weight = unpack_cols(packed_weight, bits, p_w_k, p_w_n * pack_factor).view(
            p_w_k, p_w_n, pack_factor
        )
        weight = weight.permute(0, 2, 1).reshape(input_size, output_size).contiguous()
        weight = pack_cols(weight, bits, input_size, output_size)
        # make 16 output channel as a block and transpose to the make
        # the block contigous
        weight = (
            weight.view(input_size, -1, 16 // pack_factor)
            .permute(1, 0, 2)
            .reshape(-1, input_size * 16 // pack_factor)
            .contiguous()
        )
        layer.qweight.data = weight

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        x = cpu_gemm_wna16(
            input=x,
            q_weight=layer.qweight,
            scales=layer.scales,
            zeros=layer.qzeros,
            g_idx=layer.g_idx,
            bias=bias,
            pack_factor=8,
            isa_hint=layer.isa_hint,
        )
        return x


class CPUAWQConfig(QuantizationConfig):
    """Config class for CPU AWQ"""

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        zero_point: bool,
        lm_head_quantized: bool,
        modules_to_not_convert: list[str] | None,
        full_config: dict[str, Any],
    ) -> None:
        super().__init__()
        assert weight_bits == 4
        self.pack_factor = 32 // weight_bits  # packed into int32
        self.group_size = group_size
        self.zero_point = zero_point
        self.lm_head_quantized = lm_head_quantized
        self.weight_bits = weight_bits
        self.modules_to_not_convert = modules_to_not_convert or []
        self.full_config = full_config

    def __repr__(self) -> str:
        return (
            f"AWQMarlinConfig("
            f"group_size={self.group_size}, "
            f"zero_point={self.zero_point}, "
            f"lm_head_quantized={self.lm_head_quantized}, "
            f"modules_to_not_convert={self.modules_to_not_convert})"
        )

    @classmethod
    def get_name(cls) -> "QuantizationMethods":
        return "cpu_awq"

    @classmethod
    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
        return [torch.half, torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        return -1

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return ["quantize_config.json"]

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "CPUAWQConfig":
        weight_bits = cls.get_from_keys(config, ["bits"])
        group_size = cls.get_from_keys(config, ["group_size"])
        zero_point = cls.get_from_keys(config, ["zero_point"])
        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
        modules_to_not_convert = cls.get_from_keys_or(
            config, ["modules_to_not_convert"], None
        )
        return cls(
            weight_bits,
            group_size,
            zero_point,
            lm_head_quantized,
            modules_to_not_convert,
            config,
        )

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> Optional["QuantizationMethods"]:
        quant_method = hf_quant_cfg.get("quant_method", "").lower()
        if current_platform.is_cpu() and (quant_method == "awq"):
            return cls.get_name()
        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        if isinstance(layer, LinearBase) or (
            isinstance(layer, ParallelLMHead) and self.lm_head_quantized
        ):
            if is_layer_skipped(
                prefix,
                self.modules_to_not_convert,
                self.packed_modules_mapping,
                skip_with_substr=True,
            ):
                return UnquantizedLinearMethod()
            return CPUAWQLinearMethod(self)
        return None

    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
        if self.modules_to_not_convert:
            self.modules_to_not_convert = hf_to_vllm_mapper.apply_list(
                self.modules_to_not_convert
            )

    def maybe_update_config(self, model_name: str, revision: str | None = None):
        if self.modules_to_not_convert:
            return

        unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
        metadata = get_safetensors_params_metadata(model_name, revision=revision)
        layers = {param_name.rsplit(".", 1)[0] for param_name in metadata}
        quant_layers: set[str] = {
            param_name.rsplit(".", 1)[0]
            for param_name, info in metadata.items()
            if (dtype := info.get("dtype", None))
            and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
        }
        self.modules_to_not_convert = list(layers - quant_layers)


class CPUAWQLinearMethod(LinearMethodBase):
    """Linear method for CPU AWQ.

    Args:
        quant_config: The CPU AWQ quantization config.
    """

    def __init__(self, quant_config: CPUAWQConfig) -> None:
        self.quant_config = quant_config
        assert self.quant_config.zero_point

    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,
        **extra_weight_attrs,
    ) -> None:
        del output_size
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")

        # Normalize group_size
        if self.quant_config.group_size != -1:
            group_size = self.quant_config.group_size
        else:
            group_size = input_size

        qweight = PackedvLLMParameter(
            data=torch.empty(
                input_size_per_partition,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        num_groups = input_size_per_partition // group_size

        qzeros = PackedvLLMParameter(
            data=torch.empty(
                num_groups,
                output_size_per_partition // self.quant_config.pack_factor,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            weight_loader=weight_loader,
        )

        scales = GroupQuantScaleParameter(
            data=torch.empty(
                num_groups,
                output_size_per_partition,
                dtype=params_dtype,
            ),
            input_dim=0,
            output_dim=1,
            weight_loader=weight_loader,
        )

        layer.register_parameter("qweight", qweight)
        layer.register_parameter("qzeros", qzeros)
        layer.register_parameter("scales", scales)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        torch.set_printoptions(profile="full", linewidth=5000, sci_mode=False)
        packed_weight = layer.qweight.data
        packed_zeros = layer.qzeros.data
        group_num = packed_zeros.size(0)
        bits = self.quant_config.weight_bits
        pack_factor = int(self.quant_config.pack_factor)
        input_size, packed_output_size = packed_weight.size()
        output_size = packed_output_size * pack_factor
        isa_hint = _get_isa_hint(layer.scales.dtype)
        layer.isa_hint = isa_hint

        interleave_map = (0, 4, 1, 5, 2, 6, 3, 7)
        weight = unpack_cols(
            packed_weight,
            bits,
            input_size,
            output_size,
        )
        zeros = unpack_cols(
            packed_zeros,
            bits,
            group_num,
            output_size,
        )
        weight = (
            weight.view(input_size, -1, pack_factor)[:, :, interleave_map]
            .reshape(input_size, output_size)
            .contiguous()
        )
        zeros = (
            zeros.view(group_num, -1, pack_factor)[:, :, interleave_map]
            .reshape(group_num, output_size)
            .contiguous()
        )

        zeros = pack_cols(zeros, bits, group_num, output_size).contiguous()
        # make 16 output channel as a block and transpose to
        # the make the block contigous
        weight = pack_cols(weight, bits, input_size, output_size)
        weight = (
            weight.view(input_size, -1, 16 // pack_factor)
            .permute(1, 0, 2)
            .reshape(-1, input_size * 16 // pack_factor)
            .contiguous()
        )
        layer.qweight.data = weight
        layer.qzeros.data = zeros

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        x = cpu_gemm_wna16(
            input=x,
            q_weight=layer.qweight,
            scales=layer.scales,
            zeros=layer.qzeros,
            g_idx=None,
            bias=bias,
            pack_factor=8,
            isa_hint=layer.isa_hint,
        )
        return x


def _get_isa_hint(dtype: torch.dtype) -> str:
    supports_amx = torch._C._cpu._is_amx_tile_supported()
    if supports_amx and dtype in (torch.bfloat16,):
        return "amx"
    else:
        return "vec"