aphrodite-engine/aphrodite/quantization/fp8.py

from typing import Any, Dict, List, Optional

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

import aphrodite.common.envs as envs
from aphrodite import _custom_ops as ops
from aphrodite.common.utils import is_hip, print_warning_once
from aphrodite.modeling.layers.fused_moe import FusedMoE, FusedMoEMethodBase
from aphrodite.modeling.layers.linear import (LinearBase, LinearMethodBase,
                                              UnquantizedLinearMethod)
from aphrodite.modeling.utils import set_weight_attrs
from aphrodite.platforms import current_platform
from aphrodite.quantization.base_config import (QuantizationConfig,
                                                QuantizeMethodBase)
from aphrodite.quantization.kv_cache import BaseKVCacheMethod
from aphrodite.quantization.utils.marlin_utils_fp8 import (
    apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
from aphrodite.quantization.utils.quant_utils import is_layer_skipped
from aphrodite.quantization.utils.w8a8_utils import (
    all_close_1d, apply_fp8_linear, convert_to_channelwise,
    create_per_tensor_scale_param, cutlass_fp8_supported,
    normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
    requantize_with_max_scale)

ACTIVATION_SCHEMES = ["static", "dynamic"]
APHRODITE_TEST_FORCE_FP8_MARLIN = envs.APHRODITE_TEST_FORCE_FP8_MARLIN


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 aphrodite.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
        capability = current_platform.get_device_capability()
        capability = capability[0] * 10 + capability[1]
        self.use_marlin = capability < 89 or APHRODITE_TEST_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)

        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 = Parameter(torch.empty(output_size_per_partition,
                                       input_size_per_partition,
                                       dtype=weight_dtype),
                           requires_grad=False)
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, {
            **extra_weight_attrs,
            "input_dim": 1,
            "output_dim": 0,
        })

        # 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 = create_per_tensor_scale_param(output_partition_sizes,
                                                  **extra_weight_attrs)
            layer.register_parameter("weight_scale", scale)

            # INPUT ACTIVATION SCALE
            if self.quant_config.activation_scheme == "static":
                scale = create_per_tensor_scale_param(output_partition_sizes,
                                                      **extra_weight_attrs)
                layer.register_parameter("input_scale", scale)
            else:
                layer.register_parameter("input_scale", None)

    def process_weights_after_loading(self, layer: Module) -> None:
        # 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:
            # 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, use float8_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)

        # 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, {
                "is_fp8_scale": True,
                **extra_weight_attrs
            })
            set_weight_attrs(w2_weight_scale, {
                "is_fp8_scale": True,
                **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, {
                "is_fp8_scale": True,
                **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, {
                "is_fp8_scale": True,
                **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 as dtype
            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) -> torch.Tensor:

        from aphrodite.modeling.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)

        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)