332 lines
11 KiB
Python
332 lines
11 KiB
Python
from typing import Any, Callable, Dict, List, Optional
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import torch
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from torch.nn.parameter import Parameter
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from sglang.srt.layers.linear import LinearMethodBase
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from sglang.srt.layers.parameter import ChannelQuantScaleParameter, ModelWeightParameter
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from sglang.srt.layers.quantization.base_config import (
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.fp8_kernel import (
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fp8_dtype,
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is_fp8_fnuz,
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per_token_group_quant_fp8,
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)
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from sglang.srt.layers.quantization.fp8_utils import (
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apply_fp8_linear,
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cutlass_fp8_supported,
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input_to_float8,
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normalize_e4m3fn_to_e4m3fnuz,
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)
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from sglang.srt.utils import set_weight_attrs
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_is_fp8_fnuz = is_fp8_fnuz()
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class W8A8Fp8Config(QuantizationConfig):
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"""Config class for W8A8 FP8 Quantization.
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Weight Quantization:
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- Method: Static quantization
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- Granularity: Per-channel
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- Type: Symmetric
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Activation Quantization:
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- Method: Dynamic quantization
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- Granularity: Per-token
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- Type: Symmetric
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Note:
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- For models without offline quantization, weights will be quantized during model loading
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- If CUTLASS is supported: Per-channel weight quantization is used
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- If CUTLASS is not supported: Falls back to per-tensor weight quantization
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"""
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def __init__(self, is_checkpoint_fp8_serialized: bool = False):
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.float16, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 89
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@classmethod
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def get_name(self) -> str:
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return "w8a8_fp8"
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "W8A8Fp8Config":
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quant_method = cls.get_from_keys(config, ["quant_method"])
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is_checkpoint_fp8_serialized = (
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"compressed-tensors" in quant_method or "w8a8_fp8" in quant_method
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)
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return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized)
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def get_quant_method(
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self,
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layer: torch.nn.Module,
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prefix: str,
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) -> Optional["QuantizeMethodBase"]:
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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if isinstance(layer, LinearBase):
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return W8A8Fp8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return W8A8FP8MoEMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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return []
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class W8A8Fp8LinearMethod(LinearMethodBase):
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def __init__(self, quantization_config: W8A8Fp8Config):
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self.cutlass_fp8_supported = cutlass_fp8_supported()
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self.quantization_config = quantization_config
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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weight = layer.weight
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if self.quantization_config.is_checkpoint_fp8_serialized:
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weight_scale = layer.weight_scale.detach()
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# If checkpoint offline quantized with w8a8_fp8, load the weight and weight_scale directly.
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if _is_fp8_fnuz:
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weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
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weight=weight, weight_scale=weight_scale
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)
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layer.weight = Parameter(weight.t(), requires_grad=False)
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layer.weight_scale = Parameter(weight_scale, requires_grad=False)
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else:
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# If checkpoint not offline quantized, quantize the weights with per-channel quantization.
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if self.cutlass_fp8_supported:
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# if cutlass supported, we use cutlass_scaled_mm
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# which requires per-channel quantization on weight
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qweight, weight_scale = per_token_group_quant_fp8(
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layer.weight, layer.weight.shape[-1]
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)
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weight_scale = weight_scale.t().contiguous()
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else:
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# if cutlass not supported, we fall back to use torch._scaled_mm
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# which requires per tensor quantization on weight
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qweight, weight_scale = input_to_float8(layer.weight, dtype=fp8_dtype)
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# Update the layer with the new values.
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layer.weight = Parameter(qweight.t(), requires_grad=False)
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layer.weight_scale = Parameter(weight_scale, requires_grad=False)
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layer.input_scale = None
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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weight_dtype = (
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torch.float8_e4m3fn
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if self.quantization_config.is_checkpoint_fp8_serialized
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else params_dtype
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)
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weight_loader = extra_weight_attrs.get("weight_loader")
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self.logical_widths = output_partition_sizes
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weight = ModelWeightParameter(
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data=torch.empty(
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sum(output_partition_sizes),
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input_size_per_partition,
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dtype=weight_dtype,
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight", weight)
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if self.quantization_config.is_checkpoint_fp8_serialized:
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weight_scale = ChannelQuantScaleParameter(
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data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight_scale", weight_scale)
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else:
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layer.weight_scale = None
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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):
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return apply_fp8_linear(
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x,
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layer.weight,
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layer.weight_scale,
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bias=bias,
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cutlass_fp8_supported=self.cutlass_fp8_supported,
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)
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class W8A8FP8MoEMethod:
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"""MoE method for FP8.
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Supports loading FP8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Also supports loading quantized FP16/BF16 model checkpoints with dynamic
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activation scaling. The weight scaling factor will be initialized after
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the model weights are loaded.
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Args:
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quant_config: The quantization config.
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"""
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def __new__(cls, *args, **kwargs):
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoEMethodBase
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if not hasattr(cls, "_initialized"):
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original_init = cls.__init__
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new_cls = type(
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cls.__name__,
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(FusedMoEMethodBase,),
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{
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"__init__": original_init,
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**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
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},
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)
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obj = super(new_cls, new_cls).__new__(new_cls)
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obj.__init__(*args, **kwargs)
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return obj
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return super().__new__(cls)
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def __init__(self, quant_config):
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, 2 * intermediate_size, hidden_size, dtype=fp8_dtype
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(num_experts, hidden_size, intermediate_size, dtype=fp8_dtype),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
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)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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w13_input_scale = None
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layer.register_parameter("w13_input_scale", w13_input_scale)
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w2_input_scale = None
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layer.register_parameter("w2_input_scale", w2_input_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
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layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
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layer.w13_weight_scale = Parameter(
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layer.w13_weight_scale.data, requires_grad=False
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)
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layer.w2_weight_scale = Parameter(
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layer.w2_weight_scale.data, requires_grad=False
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)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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num_fused_shared_experts: int = 0,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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activation: str = "silu",
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inplace: bool = True,
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no_combine: bool = False,
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routed_scaling_factor: Optional[float] = None,
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) -> torch.Tensor:
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from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
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from sglang.srt.layers.moe.topk import select_experts
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# Expert selection
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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num_fused_shared_experts=num_fused_shared_experts,
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custom_routing_function=custom_routing_function,
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correction_bias=correction_bias,
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routed_scaling_factor=routed_scaling_factor,
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)
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return fused_experts(
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x,
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layer.w13_weight,
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layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=inplace,
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activation=activation,
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use_fp8_w8a8=True,
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per_channel_quant=True,
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w1_scale=(layer.w13_weight_scale),
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w2_scale=(layer.w2_weight_scale),
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a1_scale=layer.w13_input_scale,
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a2_scale=layer.w2_input_scale,
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no_combine=no_combine,
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)
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