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vllm.model_executor.layers.quantization.fp8

ACTIVATION_SCHEMES module-attribute 

ACTIVATION_SCHEMES = ['static', 'dynamic']

logger module-attribute 

logger = init_logger(__name__)

CopyNumelCounter

Bases: 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.

Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

copied_numel instance-attribute 

copied_numel = 0

__init__ 

__init__()
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def __init__(self):
    super().__init__()
    self.copied_numel = 0

__torch_dispatch__ 

__torch_dispatch__(func, types, args=(), kwargs=None)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

Fp8Config

Bases: QuantizationConfig

Config class for FP8.

Source code in vllm/model_executor/layers/quantization/fp8.py 
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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_xpu_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        from vllm.model_executor.layers.quantization.ipex_quant import (
            XPUFp8LinearMethod,
            XPUFp8MoEMethod,
        )

        fp8_config = Fp8Config(
            is_checkpoint_fp8_serialized=self.is_checkpoint_fp8_serialized,
            activation_scheme=self.activation_scheme,
            ignored_layers=self.ignored_layers,
            weight_block_size=self.weight_block_size,
        )

        if isinstance(layer, LinearBase):
            if is_layer_skipped(
                prefix=prefix,
                ignored_layers=self.ignored_layers,
                fused_mapping=self.packed_modules_mapping,
            ):
                return UnquantizedLinearMethod()
            return XPUFp8LinearMethod(fp8_config)
        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)

            return XPUFp8MoEMethod(fp8_config, layer)
        elif isinstance(layer, Attention):
            return Fp8KVCacheMethod(self)
        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        if current_platform.is_xpu():
            return self.get_xpu_quant_method(layer, prefix)
        if isinstance(layer, LinearBase):
            if is_layer_skipped(
                prefix=prefix,
                ignored_layers=self.ignored_layers,
                fused_mapping=self.packed_modules_mapping,
            ):
                return UnquantizedLinearMethod()
            quant_method = Fp8LinearMethod(self)
            quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
            return quant_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

activation_scheme instance-attribute 

activation_scheme = activation_scheme

ignored_layers instance-attribute 

ignored_layers = ignored_layers or []

is_checkpoint_fp8_serialized instance-attribute 

is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized

weight_block_size instance-attribute 

weight_block_size = weight_block_size

__init__ 

__init__(
    is_checkpoint_fp8_serialized: bool = False,
    activation_scheme: str = "dynamic",
    ignored_layers: list[str] | None = None,
    weight_block_size: list[int] | None = None,
) -> None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

apply_vllm_mapper 

apply_vllm_mapper(hf_to_vllm_mapper: WeightsMapper)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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)

from_config classmethod 

from_config(config: dict[str, Any]) -> Fp8Config
Source code in vllm/model_executor/layers/quantization/fp8.py 
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@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,
    )

get_cache_scale 

get_cache_scale(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

Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

get_config_filenames classmethod 

get_config_filenames() -> list[str]
Source code in vllm/model_executor/layers/quantization/fp8.py 
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@classmethod
def get_config_filenames(cls) -> list[str]:
    return []

get_min_capability classmethod 

get_min_capability() -> int
Source code in vllm/model_executor/layers/quantization/fp8.py 
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@classmethod
def get_min_capability(cls) -> int:
    return 75

get_name classmethod 

get_name() -> QuantizationMethods
Source code in vllm/model_executor/layers/quantization/fp8.py 
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@classmethod
def get_name(cls) -> QuantizationMethods:
    return "fp8"

get_quant_method 

get_quant_method(
    layer: Module, prefix: str
) -> Optional[QuantizeMethodBase]
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def get_quant_method(
    self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
    if current_platform.is_xpu():
        return self.get_xpu_quant_method(layer, prefix)
    if isinstance(layer, LinearBase):
        if is_layer_skipped(
            prefix=prefix,
            ignored_layers=self.ignored_layers,
            fused_mapping=self.packed_modules_mapping,
        ):
            return UnquantizedLinearMethod()
        quant_method = Fp8LinearMethod(self)
        quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
        return quant_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

get_supported_act_dtypes classmethod 

get_supported_act_dtypes() -> list[dtype]
Source code in vllm/model_executor/layers/quantization/fp8.py 
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@classmethod
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
    return [torch.bfloat16, torch.half]

get_xpu_quant_method 

get_xpu_quant_method(
    layer: Module, prefix: str
) -> Optional[QuantizeMethodBase]
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def get_xpu_quant_method(
    self, layer: torch.nn.Module, prefix: str
) -> Optional["QuantizeMethodBase"]:
    from vllm.model_executor.layers.quantization.ipex_quant import (
        XPUFp8LinearMethod,
        XPUFp8MoEMethod,
    )

    fp8_config = Fp8Config(
        is_checkpoint_fp8_serialized=self.is_checkpoint_fp8_serialized,
        activation_scheme=self.activation_scheme,
        ignored_layers=self.ignored_layers,
        weight_block_size=self.weight_block_size,
    )

    if isinstance(layer, LinearBase):
        if is_layer_skipped(
            prefix=prefix,
            ignored_layers=self.ignored_layers,
            fused_mapping=self.packed_modules_mapping,
        ):
            return UnquantizedLinearMethod()
        return XPUFp8LinearMethod(fp8_config)
    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)

        return XPUFp8MoEMethod(fp8_config, layer)
    elif isinstance(layer, Attention):
        return Fp8KVCacheMethod(self)
    return None

Fp8KVCacheMethod

Bases: BaseKVCacheMethod

Supports loading kv-cache scaling factors from FP8 checkpoints.

Source code in vllm/model_executor/layers/quantization/fp8.py 
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class Fp8KVCacheMethod(BaseKVCacheMethod):
    """
    Supports loading kv-cache scaling factors from FP8 checkpoints.
    """

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

__init__ 

__init__(quant_config: Fp8Config)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def __init__(self, quant_config: Fp8Config):
    super().__init__(quant_config)

Fp8LinearMethod

Bases: 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)

Parameters:

Name Type Description Default
quant_config Fp8Config

The quantization config.

 required
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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_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():
            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.weight_block_size:
            self.act_q_group_shape = GroupShape(1, self.weight_block_size[0])
        else:
            # Use per-token quantization for better perf if dynamic and cutlass
            if not self.act_q_static and cutlass_fp8_supported():
                self.act_q_group_shape = GroupShape.PER_TOKEN
            else:
                self.act_q_group_shape = GroupShape.PER_TENSOR

        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=self.act_q_group_shape,
                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
                use_aiter_and_is_supported=self.use_aiter_and_is_supported,
            )
        else:
            self.fp8_linear = Fp8LinearOp(
                act_quant_static=self.act_q_static,
                act_quant_group_shape=self.act_q_group_shape,
            )

    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,
    ):
        maybe_create_device_identity()

        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
        if self.quant_config.is_checkpoint_fp8_serialized:
            weight = create_fp8_weight_parameter(
                output_size_per_partition, input_size_per_partition, weight_loader
            )
        else:

            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

                # 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)

                    # Delete the bookkeeping
                    del layer._loaded_numel
                    # Prevent the usual `process_weights_after_loading` call from doing
                    # anything
                    layer._already_called_process_weights_after_loading = True

                return res

            # For non-serialized checkpoints, use original dtype
            weight = ModelWeightParameter(
                data=torch.empty(
                    output_size_per_partition,
                    input_size_per_partition,
                    dtype=params_dtype,
                ),
                input_dim=1,
                output_dim=0,
                weight_loader=patched_weight_loader,
            )
        layer.register_parameter("weight", weight)

        # If checkpoint is serialized fp8, load them.
        # Otherwise, wait until process_weights_after_loading.
        if self.quant_config.is_checkpoint_fp8_serialized:
            # WEIGHT SCALE
            if not self.block_quant:
                scale = create_fp8_scale_parameter(
                    PerTensorScaleParameter,
                    output_partition_sizes,
                    input_size_per_partition,
                    None,
                    weight_loader,
                )
                set_weight_attrs(scale, {"scale_type": "weight_scale"})
                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,
                )
                set_weight_attrs(scale, {"scale_type": "weight_scale"})
                # 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)
            else:
                layer.register_parameter("input_scale", None)

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

        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 not self.quant_config.is_checkpoint_fp8_serialized:
                qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None)
                weight = qweight.t()

            # If checkpoint is fp8 per-tensor, handle that there are N scales for N
            # shards in a fused module
            else:
                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(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            out_dtype=self.out_dtype,
            input_scale=layer.input_scale,
            bias=bias,
        )

act_q_group_shape instance-attribute 

act_q_group_shape = GroupShape(1, weight_block_size[0])

act_q_static instance-attribute 

act_q_static = activation_scheme == 'static'

block_quant instance-attribute 

block_quant = weight_block_size is not None

cutlass_block_fp8_supported instance-attribute 

cutlass_block_fp8_supported = cutlass_block_fp8_supported()

fp8_linear instance-attribute 

fp8_linear = Fp8LinearOp(
    act_quant_static=act_q_static,
    act_quant_group_shape=act_q_group_shape,
)

marlin_input_dtype instance-attribute 

marlin_input_dtype = None

out_dtype instance-attribute 

out_dtype = get_default_dtype()

quant_config instance-attribute 

quant_config = quant_config

use_aiter_and_is_supported instance-attribute 

use_aiter_and_is_supported = is_linear_fp8_enabled()

use_deep_gemm instance-attribute 

use_deep_gemm = is_deep_gemm_supported()

use_marlin instance-attribute 

use_marlin = (
    not has_device_capability(89)
    or VLLM_TEST_FORCE_FP8_MARLIN
)

w8a8_block_fp8_linear instance-attribute 

w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
    weight_group_shape=GroupShape(*(weight_block_size)),
    act_quant_group_shape=act_q_group_shape,
    cutlass_block_fp8_supported=cutlass_block_fp8_supported,
    use_aiter_and_is_supported=use_aiter_and_is_supported,
)

weight_block_size instance-attribute 

weight_block_size = weight_block_size

__init__ 

__init__(quant_config: Fp8Config)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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():
        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.weight_block_size:
        self.act_q_group_shape = GroupShape(1, self.weight_block_size[0])
    else:
        # Use per-token quantization for better perf if dynamic and cutlass
        if not self.act_q_static and cutlass_fp8_supported():
            self.act_q_group_shape = GroupShape.PER_TOKEN
        else:
            self.act_q_group_shape = GroupShape.PER_TENSOR

    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=self.act_q_group_shape,
            cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
            use_aiter_and_is_supported=self.use_aiter_and_is_supported,
        )
    else:
        self.fp8_linear = Fp8LinearOp(
            act_quant_static=self.act_q_static,
            act_quant_group_shape=self.act_q_group_shape,
        )

apply 

apply(
    layer: Module, x: Tensor, bias: Tensor | None = None
) -> Tensor
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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(
        input=x,
        weight=layer.weight,
        weight_scale=layer.weight_scale,
        out_dtype=self.out_dtype,
        input_scale=layer.input_scale,
        bias=bias,
    )

create_weights 

create_weights(
    layer: Module,
    input_size_per_partition: int,
    output_partition_sizes: list[int],
    input_size: int,
    output_size: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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,
):
    maybe_create_device_identity()

    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
    if self.quant_config.is_checkpoint_fp8_serialized:
        weight = create_fp8_weight_parameter(
            output_size_per_partition, input_size_per_partition, weight_loader
        )
    else:

        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

            # 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)

                # Delete the bookkeeping
                del layer._loaded_numel
                # Prevent the usual `process_weights_after_loading` call from doing
                # anything
                layer._already_called_process_weights_after_loading = True

            return res

        # For non-serialized checkpoints, use original dtype
        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition,
                dtype=params_dtype,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=patched_weight_loader,
        )
    layer.register_parameter("weight", weight)

    # If checkpoint is serialized fp8, load them.
    # Otherwise, wait until process_weights_after_loading.
    if self.quant_config.is_checkpoint_fp8_serialized:
        # WEIGHT SCALE
        if not self.block_quant:
            scale = create_fp8_scale_parameter(
                PerTensorScaleParameter,
                output_partition_sizes,
                input_size_per_partition,
                None,
                weight_loader,
            )
            set_weight_attrs(scale, {"scale_type": "weight_scale"})
            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,
            )
            set_weight_attrs(scale, {"scale_type": "weight_scale"})
            # 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)
        else:
            layer.register_parameter("input_scale", None)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def process_weights_after_loading(self, layer: Module) -> None:
    if getattr(layer, "_already_called_process_weights_after_loading", False):
        return

    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 not self.quant_config.is_checkpoint_fp8_serialized:
            qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None)
            weight = qweight.t()

        # If checkpoint is fp8 per-tensor, handle that there are N scales for N
        # shards in a fused module
        else:
            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)

Fp8MoEMethod

Bases: 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.

Parameters:

Name Type Description Default
quant_config Fp8Config

The quantization config.

 required
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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"
        )
        self.fp8_backend = select_fp8_moe_backend(
            block_quant=self.block_quant,
            tp_size=layer.moe_parallel_config.tp_size,
            with_lora_support=self.moe.is_lora_enabled,
        )

        if self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
            if self.block_quant and self.weight_block_size != [128, 128]:
                raise NotImplementedError(
                    "FlashInfer CUTLASS FP8 MoE backend only supports block "
                    "size [128, 128]."
                )
            if layer.activation != "silu":
                raise NotImplementedError(
                    "FlashInfer CUTLASS FP8 MoE backend only supports SiLU "
                    "activation function, but got {layer.activation}."
                )
        dynamic_per_token = (
            not self.block_quant and self.quant_config.activation_scheme != "static"
        )
        if dynamic_per_token and self.fp8_backend in [
            Fp8MoeBackend.FLASHINFER_TRTLLM,
            Fp8MoeBackend.FLASHINFER_CUTLASS,
        ]:
            raise NotImplementedError(
                "FlashInfer FP8 MoE backend does not support dynamic per token "
                "activation quantization."
            )

        self.kernel: mk.FusedMoEModularKernel | None = 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

        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)

        # 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: Module,
        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.
        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        if self.moe_quant_config:
            self.kernel, self.use_inplace = make_fp8_moe_kernel(
                layer=layer,
                moe_quant_config=self.moe_quant_config,
                moe_config=self.moe,
                fp8_backend=self.fp8_backend,
            )

    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
        )

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalize | None:
        if self.fp8_backend in [
            Fp8MoeBackend.AITER,
            Fp8MoeBackend.MARLIN,
            Fp8MoeBackend.FLASHINFER_TRTLLM,
        ]:
            return None
        elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
            prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
                self.moe,
                use_deepseek_fp8_block_scale=self.block_quant,
            )
            logger.debug_once("%s", prepare_finalize.__class__.__name__)
            return prepare_finalize
        return super().maybe_make_prepare_finalize(routing_tables)

    def select_gemm_impl(
        self,
        prepare_finalize: FusedMoEPrepareAndFinalize,
        layer: torch.nn.Module,
    ) -> FusedMoEPermuteExpertsUnpermute:
        from vllm.model_executor.layers.fused_moe import (
            BatchedDeepGemmExperts,
            BatchedTritonExperts,
            TritonExperts,
            TritonOrDeepGemmExperts,
        )

        if self.fp8_backend in [Fp8MoeBackend.MARLIN, Fp8MoeBackend.AITER]:
            raise NotImplementedError(
                "Marlin and ROCm AITER are not supported with all2all yet."
            )

        assert self.moe_quant_config is not None

        if (
            prepare_finalize.activation_format
            == FusedMoEActivationFormat.BatchedExperts
        ):
            max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank()
            assert max_num_tokens_per_rank is not None

            experts_impl = (
                BatchedDeepGemmExperts
                if self.fp8_backend == Fp8MoeBackend.DEEPGEMM
                else BatchedTritonExperts
            )
            logger.debug(
                "%s(%s): max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
                experts_impl.__name__,
                self.__class__.__name__,
                max_num_tokens_per_rank,
                self.weight_block_size,
                False,
            )
            return experts_impl(
                max_num_tokens=max_num_tokens_per_rank,
                num_dispatchers=prepare_finalize.num_dispatchers(),
                quant_config=self.moe_quant_config,
            )
        elif self.moe.is_lora_enabled:
            return TritonExperts(quant_config=self.moe_quant_config)
        elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
            # Select GEMM experts with block-scale when weights are block-quantized
            experts = select_cutlass_fp8_gemm_impl(
                self.moe,
                self.moe_quant_config,
                use_deepseek_fp8_block_scale=self.block_quant,
            )
            logger.debug_once("Using %s", experts.__class__.__name__)
            return experts
        elif self.fp8_backend == Fp8MoeBackend.DEEPGEMM:
            logger.debug(
                "TritonOrDeepGemmExperts(%s): block_size=%s, per_act_token=%s",
                self.__class__.__name__,
                self.weight_block_size,
                False,
            )
            return TritonOrDeepGemmExperts(self.moe_quant_config)
        else:
            assert self.fp8_backend == Fp8MoeBackend.TRITON
            logger.debug(
                "TritonExperts(%s): block_size=%s, per_act_token=%s",
                self.__class__.__name__,
                self.weight_block_size,
                False,
            )
            return TritonExperts(self.moe_quant_config)

    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

        return 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,
        )

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

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

    def apply(
        self,
        layer: FusedMoE,
        router: FusedMoERouter,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        if 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 == "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

                e_score_correction_bias = (
                    layer.e_score_correction_bias.to(x.dtype)
                    if layer.e_score_correction_bias is not None
                    else None
                )
                routing_method_type = layer.routing_method_type
                return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
                    routing_logits=router_logits.to(torch.float32)
                    if routing_method_type == RoutingMethodType.DeepSeekV3
                    else router_logits,
                    routing_bias=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=routing_method_type,
                    routed_scaling=layer.routed_scaling_factor,
                )
            else:
                result = 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,
                )

        topk_weights, topk_ids = router.select_experts(
            hidden_states=x,
            router_logits=router_logits,
        )

        assert self.kernel is not None
        result = self.kernel(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights,
            topk_ids,
            inplace=self.use_inplace,
            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,
        )

        return result

allow_inplace property 

allow_inplace: bool

block_quant instance-attribute 

block_quant: bool = weight_block_size is not None

fp8_backend instance-attribute 

fp8_backend = select_fp8_moe_backend(
    block_quant=block_quant,
    tp_size=tp_size,
    with_lora_support=is_lora_enabled,
)

kernel instance-attribute 

kernel: FusedMoEModularKernel | None = None

quant_config instance-attribute 

quant_config = quant_config

supports_eplb property 

supports_eplb: bool

weight_block_size instance-attribute 

weight_block_size = weight_block_size

weight_scale_name instance-attribute 

weight_scale_name = (
    "weight_scale_inv" if block_quant else "weight_scale"
)

__init__ 

__init__(quant_config: Fp8Config, layer: Module)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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"
    )
    self.fp8_backend = select_fp8_moe_backend(
        block_quant=self.block_quant,
        tp_size=layer.moe_parallel_config.tp_size,
        with_lora_support=self.moe.is_lora_enabled,
    )

    if self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
        if self.block_quant and self.weight_block_size != [128, 128]:
            raise NotImplementedError(
                "FlashInfer CUTLASS FP8 MoE backend only supports block "
                "size [128, 128]."
            )
        if layer.activation != "silu":
            raise NotImplementedError(
                "FlashInfer CUTLASS FP8 MoE backend only supports SiLU "
                "activation function, but got {layer.activation}."
            )
    dynamic_per_token = (
        not self.block_quant and self.quant_config.activation_scheme != "static"
    )
    if dynamic_per_token and self.fp8_backend in [
        Fp8MoeBackend.FLASHINFER_TRTLLM,
        Fp8MoeBackend.FLASHINFER_CUTLASS,
    ]:
        raise NotImplementedError(
            "FlashInfer FP8 MoE backend does not support dynamic per token "
            "activation quantization."
        )

    self.kernel: mk.FusedMoEModularKernel | None = None

_setup_kernel 

_setup_kernel(
    layer: Module,
    w13: Tensor,
    w2: Tensor,
    w13_scale: Tensor,
    w2_scale: Tensor,
    w13_input_scale: Tensor | None,
    w2_input_scale: Tensor | None,
) -> None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def _setup_kernel(
    self,
    layer: Module,
    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.
    self.moe_quant_config = self.get_fused_moe_quant_config(layer)
    if self.moe_quant_config:
        self.kernel, self.use_inplace = make_fp8_moe_kernel(
            layer=layer,
            moe_quant_config=self.moe_quant_config,
            moe_config=self.moe,
            fp8_backend=self.fp8_backend,
        )

apply 

apply(
    layer: FusedMoE,
    router: FusedMoERouter,
    x: Tensor,
    router_logits: Tensor,
) -> Tensor | tuple[Tensor, Tensor]
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def apply(
    self,
    layer: FusedMoE,
    router: FusedMoERouter,
    x: torch.Tensor,
    router_logits: torch.Tensor,
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
    if 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 == "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

            e_score_correction_bias = (
                layer.e_score_correction_bias.to(x.dtype)
                if layer.e_score_correction_bias is not None
                else None
            )
            routing_method_type = layer.routing_method_type
            return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
                routing_logits=router_logits.to(torch.float32)
                if routing_method_type == RoutingMethodType.DeepSeekV3
                else router_logits,
                routing_bias=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=routing_method_type,
                routed_scaling=layer.routed_scaling_factor,
            )
        else:
            result = 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,
            )

    topk_weights, topk_ids = router.select_experts(
        hidden_states=x,
        router_logits=router_logits,
    )

    assert self.kernel is not None
    result = self.kernel(
        x,
        layer.w13_weight,
        layer.w2_weight,
        topk_weights,
        topk_ids,
        inplace=self.use_inplace,
        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,
    )

    return result

create_weights 

create_weights(
    layer: Module,
    num_experts: int,
    hidden_size: int,
    intermediate_size_per_partition: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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)

    # 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

get_fused_moe_quant_config 

get_fused_moe_quant_config(
    layer: Module,
) -> FusedMoEQuantConfig | None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

    return 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,
    )

maybe_make_prepare_finalize 

maybe_make_prepare_finalize(
    routing_tables: tuple[Tensor, Tensor, Tensor]
    | None = None,
) -> FusedMoEPrepareAndFinalize | None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def maybe_make_prepare_finalize(
    self,
    routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
) -> mk.FusedMoEPrepareAndFinalize | None:
    if self.fp8_backend in [
        Fp8MoeBackend.AITER,
        Fp8MoeBackend.MARLIN,
        Fp8MoeBackend.FLASHINFER_TRTLLM,
    ]:
        return None
    elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
        prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
            self.moe,
            use_deepseek_fp8_block_scale=self.block_quant,
        )
        logger.debug_once("%s", prepare_finalize.__class__.__name__)
        return prepare_finalize
    return super().maybe_make_prepare_finalize(routing_tables)

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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
    )

select_gemm_impl 

select_gemm_impl(
    prepare_finalize: FusedMoEPrepareAndFinalize,
    layer: Module,
) -> FusedMoEPermuteExpertsUnpermute
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def select_gemm_impl(
    self,
    prepare_finalize: FusedMoEPrepareAndFinalize,
    layer: torch.nn.Module,
) -> FusedMoEPermuteExpertsUnpermute:
    from vllm.model_executor.layers.fused_moe import (
        BatchedDeepGemmExperts,
        BatchedTritonExperts,
        TritonExperts,
        TritonOrDeepGemmExperts,
    )

    if self.fp8_backend in [Fp8MoeBackend.MARLIN, Fp8MoeBackend.AITER]:
        raise NotImplementedError(
            "Marlin and ROCm AITER are not supported with all2all yet."
        )

    assert self.moe_quant_config is not None

    if (
        prepare_finalize.activation_format
        == FusedMoEActivationFormat.BatchedExperts
    ):
        max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank()
        assert max_num_tokens_per_rank is not None

        experts_impl = (
            BatchedDeepGemmExperts
            if self.fp8_backend == Fp8MoeBackend.DEEPGEMM
            else BatchedTritonExperts
        )
        logger.debug(
            "%s(%s): max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
            experts_impl.__name__,
            self.__class__.__name__,
            max_num_tokens_per_rank,
            self.weight_block_size,
            False,
        )
        return experts_impl(
            max_num_tokens=max_num_tokens_per_rank,
            num_dispatchers=prepare_finalize.num_dispatchers(),
            quant_config=self.moe_quant_config,
        )
    elif self.moe.is_lora_enabled:
        return TritonExperts(quant_config=self.moe_quant_config)
    elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
        # Select GEMM experts with block-scale when weights are block-quantized
        experts = select_cutlass_fp8_gemm_impl(
            self.moe,
            self.moe_quant_config,
            use_deepseek_fp8_block_scale=self.block_quant,
        )
        logger.debug_once("Using %s", experts.__class__.__name__)
        return experts
    elif self.fp8_backend == Fp8MoeBackend.DEEPGEMM:
        logger.debug(
            "TritonOrDeepGemmExperts(%s): block_size=%s, per_act_token=%s",
            self.__class__.__name__,
            self.weight_block_size,
            False,
        )
        return TritonOrDeepGemmExperts(self.moe_quant_config)
    else:
        assert self.fp8_backend == Fp8MoeBackend.TRITON
        logger.debug(
            "TritonExperts(%s): block_size=%s, per_act_token=%s",
            self.__class__.__name__,
            self.weight_block_size,
            False,
        )
        return TritonExperts(self.moe_quant_config)

Fp8OnlineMoEMethod

Bases: 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.

Parameters:

Name Type Description Default
quant_config Fp8Config

The quantization config.

 required
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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.
    """

    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

            # 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)

                # Delete the bookkeeping
                del layer._loaded_numel
                # Prevent the usual `process_weights_after_loading` call
                # from doing anything
                layer._already_called_process_weights_after_loading = True

            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,
                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)

        # 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

        # 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,
        )

__init__ 

__init__(quant_config: Fp8Config, layer: Module)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

create_weights 

create_weights(
    layer: Module,
    num_experts: int,
    hidden_size: int,
    intermediate_size_per_partition: int,
    params_dtype: dtype,
    **extra_weight_attrs,
)
Source code in vllm/model_executor/layers/quantization/fp8.py 
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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

        # 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)

            # Delete the bookkeeping
            del layer._loaded_numel
            # Prevent the usual `process_weights_after_loading` call
            # from doing anything
            layer._already_called_process_weights_after_loading = True

        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,
            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)

    # 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

process_weights_after_loading 

process_weights_after_loading(layer: Module) -> None
Source code in vllm/model_executor/layers/quantization/fp8.py 
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def process_weights_after_loading(self, layer: Module) -> None:
    if getattr(layer, "_already_called_process_weights_after_loading", False):
        return

    # 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,
    )