from typing import Any, Dict, List, Optional

import torch
from torch.nn import Module
from torch.nn.parameter import Parameter

from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                  fused_moe)
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
    apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    all_close_1d, apply_fp8_linear, create_per_tensor_scale_param,
    cutlass_fp8_supported, per_tensor_dequantize, requantize_with_max_scale)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.utils import print_warning_once

ACTIVATION_SCHEMES = ["static", "dynamic"]

logger = init_logger(__name__)


class Fp8Config(QuantizationConfig):
    """Config class for FP8."""

    def __init__(
        self,
        is_checkpoint_fp8_serialized: bool = False,
        activation_scheme: str = "dynamic",
    ) -> None:
        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
        if is_checkpoint_fp8_serialized:
            logger.warning("Detected fp8 checkpoint. Please note that the "
                           "format is experimental and subject to change.")
        if activation_scheme not in ACTIVATION_SCHEMES:
            raise ValueError(
                f"Unsupported activation scheme {activation_scheme}")
        self.activation_scheme = activation_scheme

    @classmethod
    def get_name(cls) -> str:
        return "fp8"

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 80

    @classmethod
    def get_config_filenames(cls) -> List[str]:
        return []

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
        quant_method = cls.get_from_keys(config, ["quant_method"])
        is_checkpoint_fp8_serialized = ("fp8" in quant_method)
        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
        return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
                   activation_scheme=activation_scheme)

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["QuantizeMethodBase"]:
        from vllm.attention.layer import Attention  # Avoid circular import

        if isinstance(layer, LinearBase):
            return Fp8LinearMethod(self)
        elif isinstance(layer, FusedMoE):
            return Fp8MoEMethod(self)
        elif isinstance(layer, Attention):
            return Fp8KVCacheMethod(self)
        return None

    def get_scaled_act_names(self) -> List[str]:
        return []


class Fp8LinearMethod(LinearMethodBase):
    """Linear method for FP8.
    Supports loading FP8 checkpoints with static weight scale and
    dynamic/static activation scale.

    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
    activation scaling. The weight scaling factor will be initialized after
    the model weights are loaded.

    Limitations:
    1. Only support per-tensor quantization due to torch._scaled_mm support.
    2. Only support float8_e4m3fn data type due to the limitation of
       torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)

    Args:
        quant_config: The quantization config.
    """

    def __init__(self, quant_config: Fp8Config):
        self.quant_config = quant_config
        self.cutlass_fp8_supported = cutlass_fp8_supported()

        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
        # kernel for fast weight-only FP8 quantization
        capability = current_platform.get_device_capability()
        capability = capability[0] * 10 + capability[1]
        self.use_marlin = capability < 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,
        **extra_weight_attrs,
    ):
        del input_size, output_size
        output_size_per_partition = sum(output_partition_sizes)

        layer.logical_widths = output_partition_sizes

        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition
        layer.orig_dtype = params_dtype

        # WEIGHT
        weight_dtype = (torch.float8_e4m3fn
                        if self.quant_config.is_checkpoint_fp8_serialized else
                        params_dtype)
        weight = Parameter(torch.empty(output_size_per_partition,
                                       input_size_per_partition,
                                       dtype=weight_dtype),
                           requires_grad=False)
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, {
            **extra_weight_attrs,
            "input_dim": 1,
            "output_dim": 0,
        })

        # If checkpoint is serialized fp8, load them.
        # Otherwise, wait until process_weights_after_loading.
        if self.quant_config.is_checkpoint_fp8_serialized:
            # WEIGHT SCALE
            scale = create_per_tensor_scale_param(output_partition_sizes,
                                                  **extra_weight_attrs)
            layer.register_parameter("weight_scale", scale)

            # INPUT ACTIVATION SCALE
            if self.quant_config.activation_scheme == "static":
                scale = create_per_tensor_scale_param(output_partition_sizes,
                                                      **extra_weight_attrs)
                layer.register_parameter("input_scale", scale)

    def process_weights_after_loading(self, layer: Module) -> None:
        # If checkpoint not serialized fp8, quantize the weights.
        if not self.quant_config.is_checkpoint_fp8_serialized:
            qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
                                                         scale=None)

            # Update the layer with the new values.
            layer.weight = Parameter(qweight.t(), requires_grad=False)
            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
            layer.input_scale = None

        # If checkpoint is fp8, requantize the separately quantized logical
        # weights into a single fp8 weight with a single weight scale.
        else:
            # Dequant -> Quant with max scale.
            max_w_scale, weight = requantize_with_max_scale(
                weight=layer.weight,
                weight_scale=layer.weight_scale,
                logical_widths=layer.logical_widths,
            )

            # Update layer with new values.
            layer.weight = Parameter(weight.t(), requires_grad=False)
            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
            if self.quant_config.activation_scheme == "static":
                layer.input_scale = Parameter(layer.input_scale.max(),
                                              requires_grad=False)
            else:
                layer.input_scale = None

        if self.use_marlin:
            prepare_fp8_layer_for_marlin(layer)
            # Activations not quantized for marlin.
            del layer.input_scale

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

        if self.use_marlin:
            return apply_fp8_marlin_linear(
                input=x,
                weight=layer.weight,
                weight_scale=layer.weight_scale,
                workspace=layer.workspace,
                size_n=layer.output_size_per_partition,
                size_k=layer.input_size_per_partition,
                bias=bias)

        return apply_fp8_linear(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=layer.input_scale,
            bias=bias,
            cutlass_fp8_supported=self.cutlass_fp8_supported,
            use_per_token_if_dynamic=False)


class Fp8MoEMethod(FusedMoEMethodBase):
    """MoE method for FP8.
    Supports loading FP8 checkpoints with static weight scale and
    dynamic/static activation scale.

    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
    activation scaling. The weight scaling factor will be initialized after
    the model weights are loaded.

    Args:
        quant_config: The quantization config.
    """

    def __init__(self, quant_config: Fp8Config):
        self.quant_config = quant_config

    def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
                       intermediate_size: int, params_dtype: torch.dtype,
                       **extra_weight_attrs):

        if self.quant_config.is_checkpoint_fp8_serialized:
            params_dtype = torch.float8_e4m3fn

        # WEIGHTS
        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
                                                    2 * intermediate_size,
                                                    hidden_size,
                                                    dtype=params_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,
                                                   dtype=params_dtype),
                                       requires_grad=False)
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        # WEIGHT_SCALES
        # Allocate 2 scales for w1 and w3 respectively.
        # They will be combined to a single scale after weight loading.
        w13_scale = torch.nn.Parameter(torch.ones(num_experts,
                                                  2,
                                                  dtype=torch.float32),
                                       requires_grad=False)
        layer.register_parameter("w13_scale", w13_scale)

        w2_scale = torch.nn.Parameter(torch.ones(num_experts,
                                                 dtype=torch.float32),
                                      requires_grad=False)
        layer.register_parameter("w2_scale", w2_scale)

        # If loading fp8 checkpoint, pass the weight loaders.
        # If loading an fp16 checkpoint, do not (we will quantize in
        #   process_weights_after_loading()
        if self.quant_config.is_checkpoint_fp8_serialized:
            set_weight_attrs(w13_scale, extra_weight_attrs)
            set_weight_attrs(w2_scale, extra_weight_attrs)

        # INPUT_SCALES
        if self.quant_config.activation_scheme == "static":
            if not self.quant_config.is_checkpoint_fp8_serialized:
                raise ValueError(
                    "Found static activation scheme for checkpoint that "
                    "was not serialized fp8.")

            a13_scale = torch.nn.Parameter(torch.ones(num_experts,
                                                      dtype=torch.float32),
                                           requires_grad=False)
            layer.register_parameter("a13_scale", a13_scale)
            set_weight_attrs(a13_scale, extra_weight_attrs)

            a2_scale = torch.nn.Parameter(torch.ones(num_experts,
                                                     dtype=torch.float32),
                                          requires_grad=False)
            layer.register_parameter("a2_scale", a2_scale)
            set_weight_attrs(a2_scale, extra_weight_attrs)
        else:
            layer.a13_scale = None
            layer.a2_scale = None

    def process_weights_after_loading(self, layer: Module) -> None:

        # If checkpoint is fp16, quantize in place.
        if not self.quant_config.is_checkpoint_fp8_serialized:
            w13_weight = torch.empty_like(layer.w13_weight.data,
                                          dtype=torch.float8_e4m3fn)
            w2_weight = torch.empty_like(layer.w2_weight.data,
                                         dtype=torch.float8_e4m3fn)

            # Re-initialize w13_scale because we directly quantize
            # merged w13 weights and generate a single scaling factor.
            layer.w13_scale = torch.nn.Parameter(torch.ones(
                layer.num_experts,
                dtype=torch.float32,
                device=w13_weight.device),
                                                 requires_grad=False)
            for expert in range(layer.num_experts):
                w13_weight[expert, :, :], layer.w13_scale[
                    expert] = ops.scaled_fp8_quant(
                        layer.w13_weight.data[expert, :, :])
                w2_weight[expert, :, :], layer.w2_scale[
                    expert] = ops.scaled_fp8_quant(
                        layer.w2_weight.data[expert, :, :])
            layer.w13_weight = torch.nn.Parameter(w13_weight,
                                                  requires_grad=False)
            layer.w2_weight = torch.nn.Parameter(w2_weight,
                                                 requires_grad=False)
            return

        # If checkpoint is fp8, we need to handle that the
        # MoE kernels require single activation scale and single weight
        # scale for w13 per expert.
        else:
            # Fp8 moe kernels require a single activation scale.
            # We take the max of all the scales in case they differ.
            if self.quant_config.activation_scheme == "static":
                if layer.a13_scale is None or layer.a2_scale is None:
                    raise ValueError(
                        "QuantConfig has static quantization, but found "
                        "activation scales are None.")
                if (not all_close_1d(layer.a13_scale)
                        or not all_close_1d(layer.a2_scale)):
                    print_warning_once(
                        "Found input_scales that are not equal for "
                        "fp8 MoE layer. Using the maximum across experts "
                        "for each layer. ")
                layer.a13_scale = torch.nn.Parameter(layer.a13_scale.max(),
                                                     requires_grad=False)
                layer.a2_scale = torch.nn.Parameter(layer.a2_scale.max(),
                                                    requires_grad=False)

            # Fp8 moe kernel needs single weight scale for w13 per expert.
            # We take the max then dequant and requant each expert.
            assert layer.w13_scale is not None
            shard_size = layer.intermediate_size_per_partition
            max_w13_scales = layer.w13_scale.max(dim=1).values
            for expert_id in range(layer.num_experts):
                start = 0
                for shard_id in range(2):
                    dq_weight = per_tensor_dequantize(
                        layer.w13_weight[expert_id][start:start +
                                                    shard_size, :],
                        layer.w13_scale[expert_id][shard_id])
                    layer.w13_weight[expert_id][
                        start:start + shard_size, :], _ = ops.scaled_fp8_quant(
                            dq_weight, max_w13_scales[expert_id])
                    start += shard_size

            layer.w13_scale = torch.nn.Parameter(max_w13_scales,
                                                 requires_grad=False)
            return

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              router_logits: torch.Tensor,
              top_k: int,
              renormalize: bool = True,
              use_grouped_topk: bool = False,
              num_expert_group: Optional[int] = None,
              topk_group: Optional[int] = None) -> torch.Tensor:

        return fused_moe(x,
                         layer.w13_weight,
                         layer.w2_weight,
                         router_logits,
                         top_k,
                         renormalize=renormalize,
                         inplace=True,
                         use_fp8=True,
                         w1_scale=layer.w13_scale,
                         w2_scale=layer.w2_scale,
                         a1_scale=layer.a13_scale,
                         a2_scale=layer.a2_scale,
                         use_grouped_topk=use_grouped_topk,
                         num_expert_group=num_expert_group,
                         topk_group=topk_group)


class Fp8KVCacheMethod(QuantizeMethodBase):
    """Supports loading kv-cache scaling factors from FP8 checkpoints.
    """

    def __init__(self, quant_config: Fp8Config):
        self.quant_config = quant_config

    def create_weights(self, layer: torch.nn.Module):
        """Create "weight" (aka k_scale and v_scale) for an attention layer.

        Args:
            layer: The layer that is using the QuantizeMethodBase factory.
        """
        # Initialize the KV cache scales to -1.0, which is an invalid value.
        # If the k/v_scale appears in the checkpoint, it will be
        # overwritten when loading weights.
        layer.k_scale = Parameter(torch.tensor(-1.0), requires_grad=False)
        layer.v_scale = Parameter(torch.tensor(-1.0), requires_grad=False)

    def apply(self, layer: torch.nn.Module) -> torch.Tensor:
        raise RuntimeError("Fp8KVCacheMethod.apply should not be called.")

    def process_weights_after_loading(self, layer: Module) -> None:
        # If the kv-cache dtype is auto, we enforce the k/v_scale to be 1.0
        # regardless whether the kv-scale is available in the checkpoint.
        if layer.kv_cache_dtype != "auto":
            if layer.k_scale > 0.0 and layer.v_scale > 0.0:
                # We prefer to use separate k_scale and v_scale if present
                k_scale = layer.k_scale.to("cpu").tolist()
                v_scale = layer.v_scale.to("cpu").tolist()
            elif layer.k_scale < 0.0 and layer.v_scale < 0.0:
                # If no scales were loaded (both scales are invalid negative
                # values), use the default value of 1.0
                k_scale = Parameter(torch.tensor(1.0), requires_grad=False)
                v_scale = Parameter(torch.tensor(1.0), requires_grad=False)
            else:
                # If we find a single kv_scale in the checkpoint, we remap
                # kv_scale to k_scale during weight loading, and duplicate
                # k_scale to v_scale here
                assert layer.k_scale > 0.0
                scale_to_duplicate = max(layer.k_scale, layer.v_scale)
                k_scale = scale_to_duplicate.to("cpu").tolist()
                v_scale = scale_to_duplicate.to("cpu").tolist()

            if not isinstance(k_scale, float) or not isinstance(
                    v_scale, float):
                raise ValueError("Only support per-tensor scaling factor "
                                 "for fp8 KV cache")

            # These are used in the final Attention.forward()
            layer._k_scale = k_scale
            layer._v_scale = v_scale
            if (layer._k_scale == 1.0 and layer._v_scale == 1.0
                    and "e5m2" not in layer.kv_cache_dtype):
                print_warning_once(
                    "Using KV cache scaling factor 1.0 for fp8_e4m3. This "
                    "may cause accuracy issues. Please make sure k/v_scale "
                    "scaling factors are available in the fp8 checkpoint.")

        del layer.k_scale
        del layer.v_scale
