fp8.py

# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py

import logging
from typing import Any, Callable, Dict, List, Optional

import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
from vllm import _custom_ops as ops
from vllm.model_executor.layers.linear import LinearBase
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
    apply_fp8_marlin_linear,
    prepare_fp8_layer_for_marlin,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import is_layer_skipped
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    all_close_1d,
    apply_fp8_linear,
    convert_to_channelwise,
    cutlass_fp8_supported,
    per_tensor_dequantize,
    requantize_with_max_scale,
)
from vllm.model_executor.parameter import ModelWeightParameter, PerTensorScaleParameter

from sglang.srt.layers.fused_moe_triton import (
    FusedMoE,
    FusedMoEMethodBase,
    FusedMoeWeightScaleSupported,
)
from sglang.srt.layers.linear import LinearMethodBase, UnquantizedLinearMethod
from sglang.srt.layers.quantization.base_config import (
    QuantizationConfig,
    QuantizeMethodBase,
)
from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
from sglang.srt.utils import (
    get_bool_env_var,
    is_hip,
    print_warning_once,
    set_weight_attrs,
)

ACTIVATION_SCHEMES = ["static", "dynamic"]

logger = logging.getLogger(__name__)


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

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

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

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

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

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
        quant_method = cls.get_from_keys(config, ["quant_method"])
        is_checkpoint_fp8_serialized = "fp8" in quant_method
        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
        return cls(
            is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
            activation_scheme=activation_scheme,
            ignored_layers=ignored_layers,
        )

    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, self.ignored_layers):
                return UnquantizedLinearMethod()
            return Fp8LinearMethod(self)
        elif isinstance(layer, FusedMoE):
            return Fp8MoEMethod(self)
        elif isinstance(layer, Attention):
            return Fp8KVCacheMethod(self)
        return None

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


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

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

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

    Args:
        quant_config: The quantization config.
    """

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

        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
        # kernel for fast weight-only FP8 quantization
        self.use_marlin = get_bool_env_var("SGLANG_FORCE_FP8_MARLIN")
        # Disable marlin for ROCm
        if is_hip():
            self.use_marlin = False

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: List[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        del input_size, output_size
        output_size_per_partition = sum(output_partition_sizes)
        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

        # 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
            scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )

            scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("weight_scale", 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
                layer.register_parameter("input_scale", scale)
            else:
                layer.register_parameter("input_scale", None)

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

            # If using marlin (w8a16), kernel uses channelwise weights,
            # so extend the weight scales to be channelwise.
            if self.use_marlin:
                assert weight_scale.numel() == 1
                weight_scale = convert_to_channelwise(
                    weight_scale.expand(len(layer.logical_widths)), layer.logical_widths
                )

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

        # If checkpoint is fp8, 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
                )
            # If using marlin (w8a16), kernel uses channelwise weights,
            # so extend the weight scales to be channelwise.
            if self.use_marlin:
                weight = layer.weight
                weight_scale = convert_to_channelwise(
                    layer.weight_scale, layer.logical_widths
                )

            # If using w8a8, torch._scaled_mm needs per tensor, so
            # requantize the logical shards as a single weight.
            else:
                # Dequant -> Quant with max scale so we can run per tensor.
                weight = layer.weight
                weight_scale = layer.weight_scale

                # If ROCm, normalize the weights and scales to e4m3fnuz
                if is_hip():
                    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,
                )

            # 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)
            # Activations not quantized for marlin.
            del layer.input_scale

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

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

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


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

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

    Args:
        quant_config: The quantization config.
    """

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

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

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

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

        w2_weight = torch.nn.Parameter(
            torch.empty(
                num_experts, hidden_size, intermediate_size, dtype=params_dtype
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        # WEIGHT_SCALES
        # Allocate 2 scales for w1 and w3 respectively.
        # They will be combined to a single scale after weight loading.
        w13_weight_scale = torch.nn.Parameter(
            torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)

        w2_weight_scale = torch.nn.Parameter(
            torch.ones(num_experts, dtype=torch.float32), requires_grad=False
        )
        layer.register_parameter("w2_weight_scale", 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.TENSOR.value}
        )
        # If loading fp8 checkpoint, pass the weight loaders.
        # If loading an fp16 checkpoint, do not (we will quantize in
        #   process_weights_after_loading()
        if self.quant_config.is_checkpoint_fp8_serialized:
            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
            set_weight_attrs(w2_weight_scale, extra_weight_attrs)

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

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

        # If checkpoint is fp16, quantize in place.
        if not self.quant_config.is_checkpoint_fp8_serialized:
            # If ROCm, use float8_e4m3fnuz instead (MI300x HW)
            fp8_dtype = torch.float8_e4m3fnuz if is_hip() else torch.float8_e4m3fn
            w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype)
            w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)

            # Re-initialize w13_scale because we directly quantize
            # merged w13 weights and generate a single scaling factor.
            layer.w13_weight_scale = torch.nn.Parameter(
                torch.ones(
                    layer.num_experts, dtype=torch.float32, device=w13_weight.device
                ),
                requires_grad=False,
            )
            for expert in range(layer.num_experts):
                w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
                    ops.scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
                )
                w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
                    ops.scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
                )
            layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
            layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
            return

        # If checkpoint is fp8, we need to handle that the
        # MoE kernels require single activation scale and single weight
        # scale for w13 per expert.
        else:
            # Fp8 moe kernels require a single activation scale.
            # We take the max of all the scales in case they differ.
            if self.quant_config.activation_scheme == "static":
                if layer.w13_input_scale is None or layer.w2_input_scale is None:
                    raise ValueError(
                        "QuantConfig has static quantization, but found "
                        "activation scales are None."
                    )
                if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
                    layer.w2_input_scale
                ):
                    print_warning_once(
                        "Found input_scales that are not equal for "
                        "fp8 MoE layer. Using the maximum across experts "
                        "for each layer. "
                    )
                layer.w13_input_scale = torch.nn.Parameter(
                    layer.w13_input_scale.max(), requires_grad=False
                )
                layer.w2_input_scale = torch.nn.Parameter(
                    layer.w2_input_scale.max(), requires_grad=False
                )
            # If ROCm, normalize the weights and scales to e4m3fnuz
            if is_hip():
                # Normalize the weights and scales
                w13_weight, w13_weight_scale, w13_input_scale = (
                    normalize_e4m3fn_to_e4m3fnuz(
                        layer.w13_weight, layer.w13_weight_scale, layer.w13_input_scale
                    )
                )
                w2_weight, w2_weight_scale, w2_input_scale = (
                    normalize_e4m3fn_to_e4m3fnuz(
                        layer.w2_weight, layer.w2_weight_scale, layer.w2_input_scale
                    )
                )
                # Reset the parameter
                layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
                layer.w13_weight_scale = torch.nn.Parameter(
                    w13_weight_scale, requires_grad=False
                )
                if w13_input_scale is not None:
                    layer.w13_input_scale = torch.nn.Parameter(
                        w13_input_scale, requires_grad=False
                    )
                layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
                layer.w2_weight_scale = torch.nn.Parameter(
                    w2_weight_scale, requires_grad=False
                )
                if w2_input_scale is not None:
                    layer.w2_input_scale = torch.nn.Parameter(
                        w2_input_scale, requires_grad=False
                    )

            # Fp8 moe kernel needs single weight scale for w13 per expert.
            # We take the max then dequant and requant each expert.
            assert layer.w13_weight_scale is not None
            shard_size = layer.intermediate_size_per_partition
            max_w13_scales = layer.w13_weight_scale.max(dim=1).values
            for expert_id in range(layer.num_experts):
                start = 0
                for shard_id in range(2):
                    dq_weight = per_tensor_dequantize(
                        layer.w13_weight[expert_id][start : start + shard_size, :],
                        layer.w13_weight_scale[expert_id][shard_id],
                    )
                    layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
                        ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
                    )
                    start += shard_size

            layer.w13_weight_scale = torch.nn.Parameter(
                max_w13_scales, requires_grad=False
            )
            return

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

        from vllm.model_executor.layers.fused_moe import fused_experts

        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
        )

        return fused_experts(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=True,
            use_fp8_w8a8=True,
            w1_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            a1_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
        )


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

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