aphrodite-engine/aphrodite/quantization/fp8.py

from typing import Any, Dict, List, Optional

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

from aphrodite.modeling.layers.linear import (LinearMethodBase,
                                              set_weight_attrs)
from aphrodite.quantization.base_config import QuantizationConfig


class FP8Config(QuantizationConfig):
    """Config class for FP8."""

    @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 89
    
    @classmethod
    def get_config_filenames(cls) -> List[str]:
        return []

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "FP8Config":
        return cls()

    def get_linear_method(self) -> "Fp8LinearMethod":
        return Fp8LinearMethod(self)

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

    def rope_style(self) -> Optional[bool]:
        return None

    def quant_vocab(self) -> List[bool]:
        return [False, False]

    def support_fused_moe(self) -> bool:
        return True


class Fp8LinearMethod(LinearMethodBase):
    """Linear method for FP8.
    We now support common FP16/BF16 model checkpoints ONLY. 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

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: List[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        output_size_per_partition = sum(output_partition_sizes)
        weight = Parameter(torch.empty(output_size_per_partition,
                                       input_size_per_partition,
                                       dtype=params_dtype),
                           requires_grad=False)
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
        set_weight_attrs(weight, extra_weight_attrs)

        w_scale = Parameter(
            torch.empty(1, dtype=torch.float32),
            requires_grad=False,
        )
        layer.register_parameter("weight_scaling_factor", w_scale)

    def process_weights_after_loading(self, layer: Module) -> None:
        # Although the linear_method is propagated to all layers,
        # only linear layers invoke "create_weights". So we check
        # whether "weight_scaling_facor" is registered to determine
        # whether the layer is a linear layer that requires quantization.
        if not hasattr(layer, "weight_scaling_factor"):
            return
        
        qweight, weight_scale = per_tensor_quantize(layer.weight)
        # torch._scaled_mm requires column-major in the second
        # input (weight), so we transpose the quantized weight.
        layer.weight = Parameter(qweight.t(), requires_grad=False)
        layer.weight_scaling_factor.data.copy_(weight_scale)

    def apply_weights(self,
                      layer: torch.nn.Module,
                      x: torch.Tensor,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        qinput, x_scale = per_tensor_quantize(x)
        output, _ = torch._scaled_mm(
            qinput,
            layer.weight,
            out_dtype=x.dtype,
            scale_a=x_scale,
            scale_b=layer.weight_scaling_factor,
            bias=bias,
        )
        return output
    
    def apply_moe_weights(self, w1: Dict[str,
                                         torch.Tensor], w2: Dict[str,
                                                                 torch.Tensor],
                          x: torch.Tensor, gating_output: torch.Tensor,
                          topk: int, renormalize: bool) -> torch.Tensor:
        raise NotImplementedError


def per_tensor_quantize(tensor: torch.Tensor) -> tuple[torch.Tensor, float]:
    """Quantize a tensor using per-tensor static scaling factor.
    Args:
        tensor: The input tensor.
    """
    finfo = torch.finfo(torch.float8_e4m3fn)
    # Calculate the scale as dtype max divided by absmax.
    # Since .abs() creates a new tensor, we use aminmax to get
    # the min and max first and then calculate the absmax.
    min_val, max_val = tensor.aminmax()
    amax = min_val.abs().max(max_val.abs())
    scale = finfo.max / amax.clamp(min=1e-12)
    # scale and clamp the tensor to bring it to
    # the representative range of float8 data type
    # (as default cast is unsaturated)
    qweight = (tensor * scale).clamp(min=finfo.min, max=finfo.max)
    # Return both float8 data and the inverse scale (as float),
    # as both required as inputs to torch._scaled_mm
    qweight = qweight.to(torch.float8_e4m3fn)
    scale = scale.float().reciprocal()
    return qweight, scale