vllm.model_executor.layers.quantization.fp8

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

    def __init__(
        self,
        is_checkpoint_fp8_serialized: bool = False,
        activation_scheme: str = "dynamic",
        ignored_layers: Optional[list[str]] = None,
        weight_block_size: Optional[list[int]] = 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 80

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

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

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "Fp8Config":
        quant_method = cls.get_from_keys(config, ["quant_method"])
        is_checkpoint_fp8_serialized = ("fp8" in quant_method)
        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
        weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"],
                                                 None)
        if not ignored_layers:
            ignored_layers = cls.get_from_keys_or(config,
                                                  ["modules_to_not_convert"],
                                                  None)
        return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
                   activation_scheme=activation_scheme,
                   ignored_layers=ignored_layers,
                   weight_block_size=weight_block_size)

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

        if isinstance(layer, LinearBase):
            if is_layer_skipped(prefix=prefix,
                                ignored_layers=self.ignored_layers,
                                fused_mapping=self.packed_modules_mapping):
                return UnquantizedLinearMethod()
            return Fp8LinearMethod(self)
        elif isinstance(layer, FusedMoE):
            return Fp8MoEMethod(self, layer)
        elif isinstance(layer, Attention):
            return Fp8KVCacheMethod(self)
        return None

    def get_cache_scale(self, name: str) -> Optional[str]:
        """
        Check whether the param name matches the format for k/v cache scales
        in compressed-tensors. If this is the case, return its equivalent
        param name expected by vLLM

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

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

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

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.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

        # AITER is only supported on ROCm and only for FP8_FNUZ
        # and at the moment are MI300 series
        self.use_aiter_and_is_supported = (current_platform.is_rocm()
                                           and envs.VLLM_ROCM_USE_AITER
                                           and envs.VLLM_ROCM_USE_AITER_LINEAR
                                           and current_platform.is_fp8_fnuz())

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

        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:
            tp_size = get_tensor_model_parallel_world_size()
            assert self.quant_config.weight_block_size is not None
            layer.weight_block_size = self.quant_config.weight_block_size
            block_n, block_k = (
                self.quant_config.weight_block_size[0],
                self.quant_config.weight_block_size[1],
            )
            # Required by row parallel
            if (tp_size > 1
                    and input_size // input_size_per_partition == tp_size
                    and input_size_per_partition % block_k != 0):
                raise ValueError(
                    f"Weight input_size_per_partition = "
                    f"{input_size_per_partition} is not divisible by "
                    f"weight quantization block_k = {block_k}.")
            # Required by column parallel or enabling merged weights
            is_tp_split = (tp_size > 1 and
                           output_size // output_size_per_partition == tp_size)
            is_merged_gemm = len(output_partition_sizes) > 1
            if is_tp_split or is_merged_gemm:
                sizes_to_check = output_partition_sizes
                if not is_tp_split and is_merged_gemm:
                    # In case of merged matrices, we allow the last
                    # matrix to not be a multiple of block size
                    sizes_to_check = output_partition_sizes[:-1]
                for output_partition_size in sizes_to_check:
                    if output_partition_size % block_n != 0:
                        raise ValueError(
                            f"Weight output_partition_size = "
                            f"{output_partition_size} is not divisible by "
                            f"weight quantization block_n = {block_n}.")

        # WEIGHT
        weight_dtype = (torch.float8_e4m3fn
                        if self.quant_config.is_checkpoint_fp8_serialized else
                        params_dtype)

        weight = ModelWeightParameter(data=torch.empty(
            output_size_per_partition,
            input_size_per_partition,
            dtype=weight_dtype),
                                      input_dim=1,
                                      output_dim=0,
                                      weight_loader=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 = PerTensorScaleParameter(
                    data=torch.empty(len(output_partition_sizes),
                                     dtype=torch.float32),
                    weight_loader=weight_loader,
                )
                scale[:] = torch.finfo(torch.float32).min
                set_weight_attrs(scale, {"scale_type": "weight_scale"})
                layer.register_parameter("weight_scale", scale)
            else:
                assert self.quant_config.activation_scheme == "dynamic"
                scale = BlockQuantScaleParameter(
                    data=torch.empty(
                        (output_size_per_partition + block_n - 1) // block_n,
                        (input_size_per_partition + block_k - 1) // block_k,
                        dtype=torch.float32,
                    ),
                    input_dim=1,
                    output_dim=0,
                    weight_loader=weight_loader,
                )
                scale[:] = torch.finfo(torch.float32).min
                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.quant_config.activation_scheme == "static":
                scale = PerTensorScaleParameter(data=torch.empty(
                    len(output_partition_sizes), dtype=torch.float32),
                                                weight_loader=weight_loader)

                scale[:] = torch.finfo(torch.float32).min
                set_weight_attrs(scale, {"scale_type": "input_scale"})
                layer.register_parameter("input_scale", scale)
            else:
                layer.register_parameter("input_scale", None)

    def _maybe_pad_weight(self, weight: torch.Tensor) -> torch.Tensor:
        # Pad the weight tensor. This is an optimization on ROCm platform, which
        # can benefit from tensors located far enough from one another in memory
        if (envs.VLLM_ROCM_FP8_PADDING and current_platform.is_rocm()
                and weight.stride(-1) == 1
                and (weight.stride(-2) * weight.element_size()) % 512 == 0):
            num_pad = 256 // weight.element_size()
            weight = F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
            torch.cuda.empty_cache()
        return weight

    def process_weights_after_loading(self, layer: Module) -> None:
        size_k_first = True
        # TODO(rob): refactor block quant into separate class.
        if self.block_quant:
            assert self.quant_config.activation_scheme == "dynamic"
            size_k_first = False
            if current_platform.is_fp8_fnuz():
                weight, weight_scale_inv, _ = \
                    normalize_e4m3fn_to_e4m3fnuz(
                        weight=layer.weight,
                        weight_scale=layer.weight_scale_inv)
            else:
                weight = layer.weight.data
                weight_scale_inv = layer.weight_scale_inv.data

            weight = self._maybe_pad_weight(weight)

            # Torch.compile cannot use Parameter subclasses.
            layer.weight = Parameter(weight, requires_grad=False)
            layer.weight_scale_inv = Parameter(weight_scale_inv,
                                               requires_grad=False)

        # If checkpoint not serialized fp8, quantize the weights.
        elif not self.quant_config.is_checkpoint_fp8_serialized:
            qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
                                                         scale=None)

            # Update the layer with the new values.
            layer.weight = Parameter(qweight.t(), requires_grad=False)
            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
            # layer.input_scale is None indicates dynamic quant and scale is
            # computed from input.
            layer.input_scale = None

        # If checkpoint is fp8, handle that there are N scales for N
        # shards in a fused module
        else:
            layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data,
                                                    requires_grad=False)
            if self.quant_config.activation_scheme == "static":
                layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
                                                       requires_grad=False)

            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:
                # Dequant -> Quant with max scale so we can run per tensor.
                if current_platform.is_fp8_fnuz():
                    weight, weight_scale, input_scale = \
                        normalize_e4m3fn_to_e4m3fnuz(
                            weight=weight,
                            weight_scale=weight_scale,
                            input_scale=layer.input_scale)
                    if input_scale is not None:
                        layer.input_scale = Parameter(input_scale,
                                                      requires_grad=False)

                weight_scale, weight = requantize_with_max_scale(
                    weight=weight,
                    weight_scale=weight_scale,
                    logical_widths=layer.logical_widths,
                )

            weight = self._maybe_pad_weight(weight)
            # Update layer with new values.
            layer.weight = Parameter(weight.t(), requires_grad=False)
            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
            if self.quant_config.activation_scheme == "static":
                layer.input_scale = Parameter(layer.input_scale.max(),
                                              requires_grad=False)

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

        # On B200, if E8M0 for DeepGemm is used, we need to
        # requantize the weight and input to the specific scale
        # at the same time.
        if is_blackwell_deep_gemm_e8m0_used():
            assert layer.weight_block_size is not None
            block_sz = tuple(layer.weight_block_size)
            requant_weight_ue8m0_inplace(
                layer.weight.data,
                layer.weight_scale_inv.data if hasattr(
                    layer, "weight_scale_inv") else layer.weight_scale.data,
                block_sz,
            )

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

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

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

            return torch.ops.vllm.apply_w8a8_block_fp8_linear(
                input=x,
                weight=layer.weight,
                block_size=self.quant_config.weight_block_size,
                weight_scale=layer.weight_scale_inv,
                input_scale=layer.input_scale,
                bias=bias,
                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
                use_aiter_and_is_supported=self.use_aiter_and_is_supported,
            )

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