fp8.py

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

from typing import TYPE_CHECKING, Any

import torch
from torch.nn import Module
from torch.utils._python_dispatch import TorchDispatchMode

import vllm.envs as envs
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm import _custom_ops as ops
from vllm._aiter_ops import rocm_aiter_ops
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.logger import init_logger
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.batch_invariant import (
    vllm_is_batch_invariant,
)
from vllm.model_executor.layers.fused_moe import (
    FusedMoE,
    FusedMoEMethodBase,
    FusedMoEPermuteExpertsUnpermute,
    FusedMoEPrepareAndFinalize,
    FusedMoeWeightScaleSupported,
    MoEActivation,
)
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEQuantConfig,
)
from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
    Fp8MoeBackend,
    convert_to_fp8_moe_kernel_format,
    make_fp8_moe_kernel,
    make_fp8_moe_quant_config,
    select_fp8_moe_backend,
)
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.kernels.scaled_mm import (
    init_fp8_linear_kernel,
)
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
    apply_fi_trtllm_fp8_per_tensor_moe,
)
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    W8A8BlockFp8LinearOp,
    create_fp8_input_scale,
    create_fp8_scale_parameter,
    create_fp8_weight_parameter,
    maybe_post_process_fp8_weight_block,
    process_fp8_input_tensor_strategy_moe,
    process_fp8_weight_block_strategy,
    process_fp8_weight_tensor_strategy,
    process_fp8_weight_tensor_strategy_moe,
    validate_fp8_block_shape,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    get_marlin_input_dtype,
)
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.quant_utils import (
    GroupShape,
    is_layer_skipped,
    kFp8Dynamic128Sym,
    kFp8DynamicTensorSym,
    kFp8DynamicTokenSym,
    kFp8Static128BlockSym,
    kFp8StaticTensorSym,
)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    cutlass_block_fp8_supported,
    cutlass_fp8_supported,
    normalize_e4m3fn_to_e4m3fnuz,
)
from vllm.model_executor.model_loader.weight_utils import initialize_single_dummy_weight
from vllm.model_executor.parameter import (
    BlockQuantScaleParameter,
    ModelWeightParameter,
    PerTensorScaleParameter,
)
from vllm.model_executor.utils import replace_parameter, set_weight_attrs
from vllm.platforms import current_platform
from vllm.utils.deep_gemm import (
    is_deep_gemm_supported,
)

if TYPE_CHECKING:
    from vllm.model_executor.models.utils import WeightsMapper

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",
        ignored_layers: list[str] | None = None,
        weight_block_size: list[int] | None = None,
    ) -> None:
        super().__init__()

        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized

        if activation_scheme not in ACTIVATION_SCHEMES:
            raise ValueError(f"Unsupported activation scheme {activation_scheme}")
        self.activation_scheme = activation_scheme
        self.ignored_layers = ignored_layers or []
        if weight_block_size is not None:
            if not is_checkpoint_fp8_serialized:
                raise ValueError(
                    "The block-wise quantization only supports fp8-serialized "
                    "checkpoint for now."
                )
            if len(weight_block_size) != 2:
                raise ValueError(
                    "The quantization block size of weight must have 2 "
                    f"dimensions, but got {len(weight_block_size)} dimensions"
                )
            if activation_scheme != "dynamic":
                raise ValueError(
                    "The block-wise quantization only supports "
                    "dynamic activation scheme for now, but got "
                    f"{activation_scheme} activation scheme."
                )
        self.weight_block_size = weight_block_size

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        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 75

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

    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
        if self.ignored_layers is not None:
            self.ignored_layers = hf_to_vllm_mapper.apply_list(self.ignored_layers)

    @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"])
        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
        weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None)
        if not ignored_layers:
            ignored_layers = cls.get_from_keys_or(
                config, ["modules_to_not_convert"], None
            )
        return cls(
            is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
            activation_scheme=activation_scheme,
            ignored_layers=ignored_layers,
            weight_block_size=weight_block_size,
        )

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> "QuantizeMethodBase | None":
        if isinstance(layer, LinearBase):
            if is_layer_skipped(
                prefix=prefix,
                ignored_layers=self.ignored_layers,
                fused_mapping=self.packed_modules_mapping,
            ):
                return UnquantizedLinearMethod()
            if not self.is_checkpoint_fp8_serialized:
                online_method = Fp8OnlineLinearMethod(self)
                online_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
                return online_method
            else:
                offline_method = Fp8LinearMethod(self)
                offline_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
                return offline_method
        elif isinstance(layer, FusedMoE):
            if is_layer_skipped(
                prefix=prefix,
                ignored_layers=self.ignored_layers,
                fused_mapping=self.packed_modules_mapping,
            ):
                return UnquantizedFusedMoEMethod(layer.moe_config)
            if self.is_checkpoint_fp8_serialized:
                moe_quant_method = Fp8MoEMethod(self, layer)
            else:
                moe_quant_method = Fp8OnlineMoEMethod(self, layer)
            return moe_quant_method
        elif isinstance(layer, Attention):
            return Fp8KVCacheMethod(self)
        return None

    def get_cache_scale(self, name: str) -> str | None:
        """
        Check whether the param name matches the format for k/v cache scales
        in compressed-tensors. If this is the case, return its equivalent
        param name expected by vLLM

        :param name: param name
        :return: matching param name for KV cache scale in vLLM
        """
        if name.endswith(".output_scale") and ".k_proj" in name:
            return name.replace(".k_proj.output_scale", ".attn.k_scale")
        if name.endswith(".output_scale") and ".v_proj" in name:
            return name.replace(".v_proj.output_scale", ".attn.v_scale")
        if name.endswith(".output_scale") and ".q_proj" in name:
            return name.replace(".q_proj.output_scale", ".attn.q_scale")
        if name.endswith("self_attn.prob_output_scale"):
            return name.replace(".prob_output_scale", ".attn.prob_scale")
        # If no matches, return None
        return None


class CopyNumelCounter(TorchDispatchMode):
    """
    Tracks total number of elements modified with `copy_`. Useful for keeping
    track of weight loading where underlying weights can be arbitrarily
    transformed (such as with `narrow`) before calling copy.
    """

    def __init__(self):
        super().__init__()
        self.copied_numel = 0

    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
        if kwargs is None:
            kwargs = {}
        out = func(*args, **kwargs)
        if func == torch.ops.aten.copy_.default:
            self.copied_numel += args[0].numel()
        return out


def _copy_missing_attrs(old: torch.Tensor, new: torch.Tensor) -> None:
    """Copies any attrs present in `old` but not in `new` to `new`"""
    new_attrs = set(dir(new))
    attrs_to_set = {}
    for attr in dir(old):
        if attr not in new_attrs:
            attrs_to_set[attr] = getattr(old, attr)
    set_weight_attrs(new, attrs_to_set)


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

    Limitations:
    1. 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_block_fp8_supported = cutlass_block_fp8_supported()
        self.out_dtype = torch.get_default_dtype()

        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
        # kernel for fast weight-only FP8 quantization
        self.marlin_input_dtype = None
        self.use_marlin = (
            not current_platform.has_device_capability(89)
            or envs.VLLM_TEST_FORCE_FP8_MARLIN
        )
        # Disable marlin for rocm
        if current_platform.is_rocm() or current_platform.is_xpu():
            self.use_marlin = False
        if vllm_is_batch_invariant():
            self.use_marlin = False

        self.use_aiter_and_is_supported = rocm_aiter_ops.is_linear_fp8_enabled()
        self.use_deep_gemm = is_deep_gemm_supported()

        self.weight_block_size = self.quant_config.weight_block_size
        self.block_quant = self.weight_block_size is not None
        self.act_q_static = self.quant_config.activation_scheme == "static"

        if self.block_quant:
            assert not self.act_q_static
            assert self.weight_block_size is not None
            self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
                weight_group_shape=GroupShape(*self.weight_block_size),
                act_quant_group_shape=GroupShape(1, self.weight_block_size[0]),
                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
                use_aiter_and_is_supported=self.use_aiter_and_is_supported,
            )
        else:
            # Use per-token quantization for better perf if dynamic and cutlass
            if self.act_q_static:
                activation_quant_key = kFp8StaticTensorSym
            elif cutlass_fp8_supported():
                activation_quant_key = kFp8DynamicTokenSym
            else:
                activation_quant_key = kFp8DynamicTensorSym

            self.fp8_linear = init_fp8_linear_kernel(
                activation_quant_key=activation_quant_key,
                weight_quant_key=kFp8StaticTensorSym,
                out_dtype=torch.get_default_dtype(),
                module_name=self.__class__.__name__,
            )

    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,
    ):
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        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
        layer.weight_block_size = None

        if self.block_quant:
            assert self.weight_block_size is not None
            layer.weight_block_size = self.weight_block_size
            validate_fp8_block_shape(
                layer,
                input_size,
                output_size,
                input_size_per_partition,
                output_partition_sizes,
                self.weight_block_size,
            )

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

        # WEIGHT SCALE
        if not self.block_quant:
            scale = create_fp8_scale_parameter(
                PerTensorScaleParameter,
                output_partition_sizes,
                input_size_per_partition,
                None,
                weight_loader,
            )
            layer.register_parameter("weight_scale", scale)
        else:
            assert not self.act_q_static
            assert self.weight_block_size is not None
            scale = create_fp8_scale_parameter(
                BlockQuantScaleParameter,
                output_partition_sizes,
                input_size_per_partition,
                self.weight_block_size,
                weight_loader,
            )
            # The weight_scale_inv name is intentional for deepseekv3
            layer.register_parameter("weight_scale_inv", scale)

        # INPUT ACTIVATION SCALE
        if self.act_q_static:
            scale = create_fp8_input_scale(output_partition_sizes, weight_loader)
            set_weight_attrs(scale, {"scale_type": "input_scale"})
            layer.register_parameter("input_scale", scale)

    def process_weights_after_loading(self, layer: Module) -> None:
        size_k_first = True
        input_scale = None
        # TODO(rob): refactor block quant into separate class.
        if self.block_quant:
            assert not self.act_q_static
            size_k_first = False

            weight, weight_scale_inv = process_fp8_weight_block_strategy(
                layer.weight, layer.weight_scale_inv
            )

            # Update layer with new values
            replace_parameter(layer, "weight", weight.data)
            replace_parameter(layer, "weight_scale_inv", weight_scale_inv.data)

        # If checkpoint not serialized fp8, quantize the weights.
        else:
            # If checkpoint is fp8 per-tensor, handle that there are N scales for N
            # shards in a fused module
            weight = layer.weight
            weight_scale = layer.weight_scale

            # If using w8a8, torch._scaled_mm needs per tensor, so
            # requantize the logical shards as a single weight.
            if not self.use_marlin:
                weight, weight_scale, input_scale = process_fp8_weight_tensor_strategy(
                    weight,
                    weight_scale,
                    layer.logical_widths,
                    getattr(layer, "input_scale", None),
                )
                if self.act_q_static:
                    assert input_scale is not None
                    input_scale = input_scale.max()
            weight = weight.t()

            # Update layer with new values.
            replace_parameter(layer, "weight", weight.data)
            replace_parameter(layer, "weight_scale", weight_scale.data)

        if input_scale is not None:
            replace_parameter(layer, "input_scale", input_scale)
        else:
            layer.input_scale = None

        if self.use_marlin:
            prepare_fp8_layer_for_marlin(
                layer, size_k_first, input_dtype=self.marlin_input_dtype
            )
            # Activations not quantized for marlin.
            del layer.input_scale
            return

        if self.block_quant:
            maybe_post_process_fp8_weight_block(layer)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # if batch invariant mode is enabled, prefer DeepGEMM FP8 path
        # we will use BF16 dequant when DeepGEMM is not supported.
        if vllm_is_batch_invariant():
            if self.block_quant:
                assert self.weight_block_size is not None
                return self.w8a8_block_fp8_linear.apply(
                    input=x,
                    weight=layer.weight,
                    weight_scale=layer.weight_scale_inv,
                    input_scale=layer.input_scale,
                    bias=bias,
                )
            else:
                # per-tensor/channel: dequant to BF16 and run GEMM
                weight_fp8 = layer.weight.to(torch.bfloat16)
                weight_scale = layer.weight_scale.to(torch.bfloat16)
                if weight_scale.numel() == 1:
                    # Per-tensor: simple scalar multiplication
                    weight_bf16 = weight_fp8 * weight_scale
                else:
                    # Multiple scales (fused modules like QKV)
                    # Try to infer correct broadcasting
                    # weight is [K, N], scale could be [num_logical_weights]
                    # Need to figure out how to broadcast - for now just try
                    # direct multiplication
                    if (
                        weight_scale.dim() == 1
                        and weight_scale.shape[0] == weight_fp8.shape[0]
                    ):
                        # Per-row scaling
                        weight_bf16 = weight_fp8 * weight_scale.unsqueeze(1)
                    else:
                        # Fallback
                        weight_bf16 = weight_fp8 * weight_scale
                return torch.nn.functional.linear(x, weight_bf16.t(), bias)

        if self.use_marlin:
            if self.block_quant:
                weight_scale = layer.weight_scale_inv
            else:
                weight_scale = layer.weight_scale

            return apply_fp8_marlin_linear(
                input=x,
                weight=layer.weight,
                weight_scale=weight_scale,
                workspace=layer.workspace,
                size_n=layer.output_size_per_partition,
                size_k=layer.input_size_per_partition,
                input_dtype=self.marlin_input_dtype,
                bias=bias,
            )

        if self.block_quant:
            assert self.weight_block_size is not None

            return self.w8a8_block_fp8_linear.apply(
                input=x,
                weight=layer.weight,
                weight_scale=layer.weight_scale_inv,
                input_scale=layer.input_scale,
                bias=bias,
            )

        return self.fp8_linear.apply_weights(layer, x, bias)


class Fp8OnlineLinearMethod(Fp8LinearMethod):
    """Online version of Fp8LinearMethod, loads the fp16/bf16 checkpoint
    and quantized the weights during loading."""

    uses_meta_device: bool = True

    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,
    ):
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        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
        layer.weight_block_size = None

        # WEIGHT
        def patched_weight_loader(param, loaded_weight, *args, **kwargs):
            # track how many elements we have updated
            if not hasattr(layer, "_loaded_numel"):
                layer._loaded_numel = 0

                # when the first `loaded_weight` is about to be
                # loaded to `param`, materialize `param` just-in-time
                weight = ModelWeightParameter(
                    data=torch.empty_like(layer.weight, device=layer._load_device),
                    input_dim=1,
                    output_dim=0,
                    weight_loader=patched_weight_loader,
                )
                _copy_missing_attrs(layer.weight, weight)
                layer.register_parameter("weight", weight)
                del layer._load_device

            # refresh the reference to `param` to reflect just-in-time
            # materialization
            param = layer.weight

            # load the current weight chunk
            copy_numel_counter = CopyNumelCounter()
            with copy_numel_counter:
                res = weight_loader(param, loaded_weight, *args, **kwargs)  # type: ignore[misc]
            layer._loaded_numel += copy_numel_counter.copied_numel

            # if we have loaded all of the elements, call
            # process_weights_after_loading
            target_loaded_numel = layer.weight.numel()
            if layer._loaded_numel == target_loaded_numel:
                self.process_weights_after_loading(layer)

                # Prevent the usual `process_weights_after_loading` call from doing
                # anything
                layer._already_called_process_weights_after_loading = True

                # Note that we keep `layer._loaded_numel` around just in case
                # there is logic added to vllm in the future which calls a
                # weight loader twice - we do not want to re-initialize in
                # that case.

            return res

        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition,
                # materialized just-in-time in `patched_weight_loader`
                device="meta",
                dtype=params_dtype,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=patched_weight_loader,
        )
        # stash the correct device for `patched_weight_loader`
        layer._load_device = torch.get_default_device()
        layer.register_parameter("weight", weight)

    def process_weights_after_loading(self, layer: Module) -> None:
        if getattr(layer, "_already_called_process_weights_after_loading", False):
            return

        # deferred initialization of randomly initialized weights for the
        # `--load_format dummy` feature
        if layer.weight.device == torch.device("meta"):
            weight = ModelWeightParameter(
                data=torch.empty_like(layer.weight, device=layer._load_device),
                input_dim=1,
                output_dim=0,
                weight_loader=layer.weight.weight_loader,
            )
            _copy_missing_attrs(layer.weight, weight)
            layer.register_parameter("weight", weight)
            initialize_single_dummy_weight(layer.weight)

        # TODO(future): support block_quant in online quant path
        assert not self.block_quant

        layer.input_scale = None
        qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None)
        weight = qweight.t()

        # Update layer with new values.
        replace_parameter(layer, "weight", weight.data)
        replace_parameter(layer, "weight_scale", weight_scale.data)

        if self.use_marlin:
            size_k_first = True
            prepare_fp8_layer_for_marlin(
                layer, size_k_first, input_dtype=self.marlin_input_dtype
            )
            # Activations not quantized for marlin.

        # Prevent duplicate processing (e.g., during weight reload)
        layer._already_called_process_weights_after_loading = True


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, layer: torch.nn.Module):
        super().__init__(layer.moe_config)
        self.quant_config = quant_config
        self.weight_block_size = self.quant_config.weight_block_size
        self.block_quant: bool = self.weight_block_size is not None
        self.weight_scale_name = (
            "weight_scale_inv" if self.block_quant else "weight_scale"
        )

        # Set weight key and activation key for kernel compatibility
        if self.block_quant:
            weight_key = kFp8Static128BlockSym
            activation_key = kFp8Dynamic128Sym
        else:
            weight_key = kFp8StaticTensorSym
            activation_key = (
                kFp8StaticTensorSym
                if self.quant_config.activation_scheme == "static"
                else kFp8DynamicTensorSym
            )

        # Select Fp8 MoE backend
        self.fp8_backend, self.experts_cls = select_fp8_moe_backend(
            config=self.moe,
            weight_key=weight_key,
            activation_key=activation_key,
            allow_vllm_cutlass=False,
        )

    def create_weights(
        self,
        layer: Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        layer.intermediate_size_per_partition = intermediate_size_per_partition
        layer.hidden_size = hidden_size
        layer.num_experts = num_experts
        layer.orig_dtype = params_dtype
        layer.weight_block_size = None

        assert self.quant_config.is_checkpoint_fp8_serialized
        params_dtype = torch.float8_e4m3fn

        if self.block_quant:
            assert self.weight_block_size is not None
            layer.weight_block_size = self.weight_block_size
            tp_size = get_tensor_model_parallel_world_size()
            block_n, block_k = (
                self.weight_block_size[0],
                self.weight_block_size[1],
            )
            # NOTE: To ensure proper alignment of the block-wise quantization
            # scales, the output_size of the weights for both the gate and up
            # layers must be divisible by block_n.
            # Required by column parallel or enabling merged weights
            if intermediate_size_per_partition % block_n != 0:
                raise ValueError(
                    f"The output_size of gate's and up's weight = "
                    f"{intermediate_size_per_partition} is not divisible by "
                    f"weight quantization block_n = {block_n}."
                )
            if tp_size > 1 and intermediate_size_per_partition % block_k != 0:
                # Required by row parallel
                raise ValueError(
                    f"The input_size of down's weight = "
                    f"{intermediate_size_per_partition} is not divisible by "
                    f"weight quantization block_k = {block_k}."
                )

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

        # BIASES (for models like GPT-OSS that have biased MoE)
        if self.moe.has_bias:
            w13_bias = torch.nn.Parameter(
                torch.zeros(
                    num_experts,
                    2 * intermediate_size_per_partition,
                    dtype=layer.orig_dtype,
                ),
                requires_grad=False,
            )
            layer.register_parameter("w13_bias", w13_bias)
            set_weight_attrs(w13_bias, extra_weight_attrs)
            w2_bias = torch.nn.Parameter(
                torch.zeros(num_experts, hidden_size, dtype=layer.orig_dtype),
                requires_grad=False,
            )
            layer.register_parameter("w2_bias", w2_bias)
            set_weight_attrs(w2_bias, extra_weight_attrs)

        # WEIGHT_SCALES
        if not self.block_quant:
            # For per-tensor quant, the scales are per expert and weight.
            w13_scale_data = torch.ones(num_experts, 2, dtype=torch.float32)
            w2_scale_data = torch.ones(num_experts, dtype=torch.float32)
        else:
            # For block quant, the scales are per block (typically 128x128).
            w13_scale_data = torch.ones(
                num_experts,
                2 * ((intermediate_size_per_partition + block_n - 1) // block_n),
                (hidden_size + block_k - 1) // block_k,
                dtype=torch.float32,
            )
            w2_scale_data = torch.ones(
                num_experts,
                (hidden_size + block_n - 1) // block_n,
                (intermediate_size_per_partition + block_k - 1) // block_k,
                dtype=torch.float32,
            )
        w13_weight_scale = torch.nn.Parameter(w13_scale_data, requires_grad=False)
        w2_weight_scale = torch.nn.Parameter(w2_scale_data, requires_grad=False)
        # Note: name is weight_scale for tensor, weight_scale_inv for block.
        layer.register_parameter(f"w13_{self.weight_scale_name}", w13_weight_scale)
        layer.register_parameter(f"w2_{self.weight_scale_name}", w2_weight_scale)

        # Add the quantization method used (per tensor/grouped/channel)
        # to ensure the weight scales are loaded in properly
        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
            if self.block_quant
            else {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
        )
        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
        set_weight_attrs(w2_weight_scale, extra_weight_attrs)

        # INPUT_SCALES
        if self.quant_config.activation_scheme == "static":
            assert not self.block_quant
            w13_input_scale = torch.nn.Parameter(
                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
            )
            layer.register_parameter("w13_input_scale", w13_input_scale)
            set_weight_attrs(w13_input_scale, extra_weight_attrs)

            w2_input_scale = torch.nn.Parameter(
                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
            )
            layer.register_parameter("w2_input_scale", w2_input_scale)
            set_weight_attrs(w2_input_scale, extra_weight_attrs)

        else:
            layer.w13_input_scale = None
            layer.w2_input_scale = None

    def _setup_kernel(
        self,
        layer: FusedMoE,
        w13: torch.Tensor,
        w2: torch.Tensor,
        w13_scale: torch.Tensor,
        w2_scale: torch.Tensor,
        w13_input_scale: torch.Tensor | None,
        w2_input_scale: torch.Tensor | None,
    ) -> None:
        # Shuffle weights to runtime format.
        w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
            fp8_backend=self.fp8_backend,
            layer=layer,
            w13=w13,
            w2=w2,
            w13_scale=w13_scale,
            w2_scale=w2_scale,
            w13_input_scale=w13_input_scale,
            w2_input_scale=w2_input_scale,
        )

        # Replace parameters with updated versions. Note that this helper
        # function ensures the replacement is compatible with RL weight reloads.
        replace_parameter(layer, "w13_weight", w13)
        replace_parameter(layer, "w2_weight", w2)
        replace_parameter(layer, f"w13_{self.weight_scale_name}", w13_scale)
        replace_parameter(layer, f"w2_{self.weight_scale_name}", w2_scale)

        # Setup modular kernel for TP case and naive DP/EP case.
        # In non-naive DP/EP case, we will create a ModularKernelMethod.
        # TODO(rob): unify these so FP8MoEMethod owns the ModularKernel
        # in both cases.
        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        if self.moe_quant_config:
            assert self.experts_cls is not None
            self.moe_mk = 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._maybe_init_expert_routing_tables(),
                shared_experts=layer.shared_experts,
            )

    def process_weights_after_loading(self, layer: Module) -> None:
        if getattr(layer, "_already_called_process_weights_after_loading", False):
            return

        # Allow for accessing weights and scales in standard way.
        w13 = layer.w13_weight
        w2 = layer.w2_weight
        w13_scale = getattr(layer, f"w13_{self.weight_scale_name}")
        w2_scale = getattr(layer, f"w2_{self.weight_scale_name}")
        w13_input_scale = layer.w13_input_scale
        w2_input_scale = layer.w2_input_scale

        # MI300x and MI325x use FNUZ format for FP8. Convert if needed.
        if current_platform.is_fp8_fnuz():
            w13, w13_scale, w13_input_scale = normalize_e4m3fn_to_e4m3fnuz(
                w13,
                w13_scale,
                w13_input_scale,
            )
            w2, w2_scale, w2_input_scale = normalize_e4m3fn_to_e4m3fnuz(
                w2,
                w2_scale,
                w2_input_scale,
            )

        # Per tensor kernels require single activation scale. Use the max.
        if self.quant_config.activation_scheme == "static":
            assert not self.block_quant
            assert w13_input_scale is not None and w2_input_scale is not None
            w13_input_scale, w2_input_scale = process_fp8_input_tensor_strategy_moe(
                w13_input_scale, w2_input_scale
            )
            replace_parameter(layer, "w13_input_scale", w13_input_scale)
            replace_parameter(layer, "w2_input_scale", w2_input_scale)

        # Per tensor kernels require single weight scale for w13 per expert, but
        # on disk there is a scale for w1 and w3. Use the max to requantize.
        if not self.block_quant:
            shard_size = layer.intermediate_size_per_partition
            w13, w13_scale = process_fp8_weight_tensor_strategy_moe(
                w13, w13_scale, shard_size, layer.local_num_experts
            )

        # Shuffle weights to runtime format and setup kernel.
        self._setup_kernel(
            layer, w13, w2, w13_scale, w2_scale, w13_input_scale, w2_input_scale
        )

        # Prevent duplicate processing (e.g., during weight reload)
        layer._already_called_process_weights_after_loading = True

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

    def select_gemm_impl(
        self,
        prepare_finalize: FusedMoEPrepareAndFinalize,
        layer: torch.nn.Module,
    ) -> FusedMoEPermuteExpertsUnpermute:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        # TRTLLM does not use Modular Kernel.
        if self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM:
            return None

        w1_scale = getattr(layer, f"w13_{self.weight_scale_name}")
        w2_scale = getattr(layer, f"w2_{self.weight_scale_name}")
        a1_scale = layer.w13_input_scale
        a2_scale = layer.w2_input_scale

        quant_config = make_fp8_moe_quant_config(
            fp8_backend=self.fp8_backend,
            w1_scale=w1_scale,
            w2_scale=w2_scale,
            a1_scale=a1_scale,
            a2_scale=a2_scale,
            block_shape=self.weight_block_size,
        )

        # Inject biases into the quant config if the model has them
        # (e.g. GPT-OSS biased MoE)
        if quant_config is not None and self.moe.has_bias:
            w13_bias = getattr(layer, "w13_bias", None)
            w2_bias = getattr(layer, "w2_bias", None)
            if w13_bias is not None:
                quant_config._w1.bias = w13_bias
            if w2_bias is not None:
                quant_config._w2.bias = w2_bias

        return quant_config

    @property
    def supports_eplb(self) -> bool:
        return True

    @property
    def is_monolithic(self) -> bool:
        return self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert self.is_monolithic
        assert self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM

        # TODO(rob): convert this to MK.
        if layer.enable_eplb:
            raise NotImplementedError("EPLB not supported for `Fp8MoEMethod` yet.")
        assert layer.activation == MoEActivation.SILU, (
            f"Expected 'silu' activation but got {layer.activation}"
        )

        if self.block_quant:
            import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe  # noqa: E501, F401

            return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
                routing_logits=router_logits,
                routing_bias=layer.e_score_correction_bias,
                x=x,
                w13_weight=layer.w13_weight,
                w13_weight_scale_inv=layer.w13_weight_scale_inv,
                w2_weight=layer.w2_weight,
                w2_weight_scale_inv=layer.w2_weight_scale_inv,
                global_num_experts=layer.global_num_experts,
                top_k=layer.top_k,
                num_expert_group=layer.num_expert_group,
                topk_group=layer.topk_group,
                intermediate_size=layer.intermediate_size_per_partition,
                expert_offset=layer.ep_rank * layer.local_num_experts,
                local_num_experts=layer.local_num_experts,
                block_shape=self.weight_block_size,
                routing_method_type=layer.routing_method_type,
                routed_scaling=layer.routed_scaling_factor,
            )
        else:
            return apply_fi_trtllm_fp8_per_tensor_moe(
                layer=layer,
                hidden_states=x,
                router_logits=router_logits,
                routing_bias=layer.e_score_correction_bias,
                global_num_experts=layer.global_num_experts,
                top_k=layer.top_k,
                num_expert_group=layer.num_expert_group,
                topk_group=layer.topk_group,
                apply_router_weight_on_input=layer.apply_router_weight_on_input,
            )

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert self.moe_mk is not None
        assert not self.is_monolithic
        return self.moe_mk(
            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_input=shared_experts_input,
        )


class Fp8OnlineMoEMethod(Fp8MoEMethod):
    """MoE method for online FP8 quantization.
    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.
    """

    uses_meta_device: bool = True

    def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
        super().__init__(quant_config, layer)
        assert not quant_config.is_checkpoint_fp8_serialized
        assert quant_config.activation_scheme == "dynamic"
        assert quant_config.weight_block_size is None

    def create_weights(
        self,
        layer: Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        layer.intermediate_size_per_partition = intermediate_size_per_partition
        layer.hidden_size = hidden_size
        layer.num_experts = num_experts
        layer.orig_dtype = params_dtype
        layer.weight_block_size = None

        # We are doing online quantization, patch the weight loaded
        # to call `process_weights_after_loading` in a streaming fashion
        # as soon as the last weight chunk is loaded.
        weight_loader = extra_weight_attrs["weight_loader"]
        # create a new holder to prevent modifying behavior of any other
        # objects which might depend on the old one
        new_extra_weight_attrs = extra_weight_attrs

        def patched_weight_loader(param, loaded_weight, *args, **kwargs):
            # add a counter to track how many elements we have updated
            if not hasattr(layer, "_loaded_numel"):
                layer._loaded_numel = 0

                # save the ids of original w13 and w2 so that we can
                # distinguish which one `param` should map to further
                # down in this file
                layer._w13_weight_orig_id = id(layer.w13_weight)
                layer._w2_weight_orig_id = id(layer.w2_weight)

                # when the first `loaded_weight` is about to be
                # loaded to `param`, materialize `param` just-in-time

                w13_weight = torch.nn.Parameter(
                    torch.empty_like(layer.w13_weight, device=layer._load_device),
                    requires_grad=False,
                )
                set_weight_attrs(w13_weight, extra_weight_attrs)
                _copy_missing_attrs(layer.w13_weight, w13_weight)
                layer.register_parameter("w13_weight", w13_weight)

                w2_weight = torch.nn.Parameter(
                    torch.empty_like(layer.w2_weight, device=layer._load_device),
                    requires_grad=False,
                )
                set_weight_attrs(w2_weight, extra_weight_attrs)
                _copy_missing_attrs(layer.w2_weight, w2_weight)
                layer.register_parameter("w2_weight", w2_weight)
                del layer._load_device

            # refresh the reference to `param` to reflect just-in-time
            # materialization
            if id(param) == layer._w13_weight_orig_id:
                param = layer.w13_weight
            elif id(param) == layer._w2_weight_orig_id:
                param = layer.w2_weight

            # load the current weight chunk
            copy_numel_counter = CopyNumelCounter()
            with copy_numel_counter:
                res = weight_loader(param, loaded_weight, *args, **kwargs)  # type: ignore[misc]
            layer._loaded_numel += copy_numel_counter.copied_numel

            # if we have loaded all of the elements, call
            # process_weights_after_loading
            target_loaded_numel = layer.w13_weight.numel() + layer.w2_weight.numel()
            if layer._loaded_numel == target_loaded_numel:
                self.process_weights_after_loading(layer)

                # Prevent the usual `process_weights_after_loading` call
                # from doing anything
                layer._already_called_process_weights_after_loading = True

                # Note that we keep `layer._loaded_numel`,
                # `layer._w13_weight_orig_id` and `layer._w2_weight_orig_id`
                # around because if EP is on, weight loaders for non-local
                # experts will run but not actually copy any elements, and we
                # need to not re-initialize in that case.

            return res

        new_extra_weight_attrs["weight_loader"] = patched_weight_loader
        extra_weight_attrs = new_extra_weight_attrs

        # WEIGHTS
        w13_weight = torch.nn.Parameter(
            torch.empty(
                num_experts,
                2 * intermediate_size_per_partition,
                hidden_size,
                # materialized just-in-time in `patched_weight_loader`
                device="meta",
                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_per_partition,
                # materialized just-in-time in `patched_weight_loader`
                device="meta",
                dtype=params_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)
        # stash the correct device for `patched_weight_loader`
        layer._load_device = torch.get_default_device()

        # BIASES (for models like GPT-OSS that have biased MoE)
        if self.moe.has_bias:
            # Use the original weight_loader (not patched) for biases
            orig_extra_weight_attrs = dict(extra_weight_attrs)
            orig_extra_weight_attrs["weight_loader"] = weight_loader
            w13_bias = torch.nn.Parameter(
                torch.zeros(
                    num_experts,
                    2 * intermediate_size_per_partition,
                    dtype=layer.orig_dtype,
                ),
                requires_grad=False,
            )
            layer.register_parameter("w13_bias", w13_bias)
            set_weight_attrs(w13_bias, orig_extra_weight_attrs)
            w2_bias = torch.nn.Parameter(
                torch.zeros(num_experts, hidden_size, dtype=layer.orig_dtype),
                requires_grad=False,
            )
            layer.register_parameter("w2_bias", w2_bias)
            set_weight_attrs(w2_bias, orig_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_weight_scale = torch.nn.Parameter(
            torch.ones(num_experts, dtype=torch.float32), requires_grad=False
        )
        w2_weight_scale = torch.nn.Parameter(
            torch.ones(num_experts, dtype=torch.float32), requires_grad=False
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        layer.register_parameter("w2_weight_scale", w2_weight_scale)
        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
        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: Module) -> None:
        if getattr(layer, "_already_called_process_weights_after_loading", False):
            return

        # deferred initialization of randomly initialized weights for the
        # `--load_format dummy` feature
        if layer.w13_weight.device == torch.device("meta"):
            w13_weight = torch.nn.Parameter(
                torch.empty_like(layer.w13_weight, device=layer._load_device),
                requires_grad=False,
            )
            set_weight_attrs(
                w13_weight, {"weight_loader": layer.w13_weight.weight_loader}
            )
            _copy_missing_attrs(layer.w13_weight, w13_weight)
            layer.register_parameter("w13_weight", w13_weight)
            initialize_single_dummy_weight(layer.w13_weight)
        if layer.w2_weight.device == torch.device("meta"):
            w2_weight = torch.nn.Parameter(
                torch.empty_like(layer.w2_weight, device=layer._load_device),
                requires_grad=False,
            )
            set_weight_attrs(
                w2_weight, {"weight_loader": layer.w2_weight.weight_loader}
            )
            _copy_missing_attrs(layer.w2_weight, w2_weight)
            layer.register_parameter("w2_weight", w2_weight)
            initialize_single_dummy_weight(layer.w2_weight)

        # If checkpoint is fp16, quantize in place.
        fp8_dtype = current_platform.fp8_dtype()
        w13 = torch.empty_like(layer.w13_weight, dtype=fp8_dtype)
        w2 = torch.empty_like(layer.w2_weight, dtype=fp8_dtype)
        w13_scale = layer.w13_weight_scale
        w2_scale = layer.w2_weight_scale

        for expert in range(layer.local_num_experts):
            w13[expert, :, :], w13_scale[expert] = ops.scaled_fp8_quant(
                layer.w13_weight[expert, :, :]
            )
            w2[expert, :, :], w2_scale[expert] = ops.scaled_fp8_quant(
                layer.w2_weight[expert, :, :]
            )

        # Shuffle weights to runtime format and setup kernel.
        self._setup_kernel(
            layer,
            w13,
            w2,
            w13_scale,
            w2_scale,
            layer.w13_input_scale,
            layer.w2_input_scale,
        )

        # Prevent duplicate processing (e.g., during weight reload)
        layer._already_called_process_weights_after_loading = True


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

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