1238 lines
49 KiB
Python
1238 lines
49 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
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from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
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import torch
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import torch.nn.functional as F
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from torch.nn import Module
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from torch.nn.parameter import Parameter
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try:
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
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apply_fp8_marlin_linear,
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prepare_fp8_layer_for_marlin,
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)
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MARLIN_FP8_AVAILABLE = True
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except ImportError:
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MARLIN_FP8_AVAILABLE = False
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def dummy_func(*args, **kwargs):
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raise ImportError(
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"marlin FP8 requires some operators from vllm. Please install vllm."
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)
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apply_fp8_marlin_linear = prepare_fp8_layer_for_marlin = dummy_func
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
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from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
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from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
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from sglang.srt.layers.moe.token_dispatcher.base import DispatchOutputChecker
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from sglang.srt.layers.parameter import (
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BlockQuantScaleParameter,
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ModelWeightParameter,
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PerTensorScaleParameter,
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)
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from sglang.srt.layers.quantization.base_config import (
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FusedMoEMethodBase,
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LinearMethodBase,
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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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scaled_fp8_quant,
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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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can_auto_enable_marlin_fp8,
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cutlass_fp8_supported,
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dispatch_w8a8_block_fp8_linear,
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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.layers.quantization.kv_cache import BaseKVCacheMethod
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from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
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from sglang.srt.layers.quantization.utils import (
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all_close_1d,
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convert_to_channelwise,
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is_layer_skipped,
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per_tensor_dequantize,
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requantize_with_max_scale,
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)
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from sglang.srt.utils import (
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cpu_has_amx_support,
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get_bool_env_var,
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is_cpu,
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is_cuda,
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is_hip,
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is_npu,
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is_sm90_supported,
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is_sm100_supported,
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log_info_on_rank0,
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next_power_of_2,
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print_warning_once,
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set_weight_attrs,
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use_intel_amx_backend,
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)
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if TYPE_CHECKING:
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from sglang.srt.layers.moe.token_dispatcher import (
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CombineInput,
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DispatchOutput,
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StandardDispatchOutput,
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)
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from sglang.srt.layers.moe.topk import TopKOutput
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from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
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_is_hip = is_hip()
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_is_cuda = is_cuda()
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_is_npu = is_npu()
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_is_cpu_amx_available = cpu_has_amx_support()
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_is_cpu = is_cpu()
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_is_fp8_fnuz = is_fp8_fnuz()
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_use_hip_int4 = get_bool_env_var("SGLANG_INT4_WEIGHT")
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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if _is_hip and (_use_aiter or _use_hip_int4):
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from aiter import ActivationType, QuantType
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from aiter.fused_moe import fused_moe
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from aiter.ops.shuffle import shuffle_weight
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ACTIVATION_SCHEMES = ["static", "dynamic"]
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logger = logging.getLogger(__name__)
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class Fp8Config(QuantizationConfig):
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"""Config class for FP8."""
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def __init__(
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self,
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is_checkpoint_fp8_serialized: bool = False,
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activation_scheme: str = "dynamic",
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ignored_layers: Optional[List[str]] = None,
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weight_block_size: List[int] = None,
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) -> None:
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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if is_checkpoint_fp8_serialized:
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log_info_on_rank0(logger, "Detected fp8 checkpoint.")
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if activation_scheme not in ACTIVATION_SCHEMES:
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raise ValueError(f"Unsupported activation scheme {activation_scheme}")
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self.activation_scheme = activation_scheme
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self.ignored_layers = ignored_layers or []
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if weight_block_size is not None:
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if not is_checkpoint_fp8_serialized:
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raise ValueError(
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f"The block-wise quantization only supports fp8-serialized checkpoint for now."
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)
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if len(weight_block_size) != 2:
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raise ValueError(
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f"The quantization block size of weight must have 2 dimensions, but got {len(weight_block_size)} dimensions."
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)
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if activation_scheme != "dynamic":
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raise ValueError(
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f"The block-wise quantization only supports dynamic activation scheme for now, but got {activation_scheme} activation scheme."
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)
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self.weight_block_size = weight_block_size
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@classmethod
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def get_name(cls) -> str:
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return "fp8"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.bfloat16, torch.half]
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@classmethod
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def get_min_capability(cls) -> int:
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return 80
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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]) -> Fp8Config:
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quant_method = cls.get_from_keys(config, ["quant_method"])
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is_checkpoint_fp8_serialized = "fp8" in quant_method
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activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
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ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
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weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None)
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return cls(
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is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
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activation_scheme=activation_scheme,
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ignored_layers=ignored_layers,
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weight_block_size=weight_block_size,
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)
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def get_quant_method(
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self, layer: torch.nn.Module, 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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if is_layer_skipped(prefix, self.ignored_layers):
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return UnquantizedLinearMethod()
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return Fp8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return Fp8MoEMethod(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 Fp8LinearMethod(LinearMethodBase):
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"""Linear 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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Limitations:
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1. Only support per-tensor quantization due to torch._scaled_mm support.
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2. Only support float8_e4m3fn data type due to the limitation of
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torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
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Args:
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quant_config: The quantization config.
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"""
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def __init__(self, quant_config: Union[Fp8Config, W4AFp8Config]):
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self.quant_config = quant_config
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self.cutlass_fp8_supported = cutlass_fp8_supported()
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# For GPUs that lack FP8 hardware support, we can leverage the Marlin
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# kernel for fast weight-only FP8 quantization
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self.use_marlin = False
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if _is_cuda and MARLIN_FP8_AVAILABLE:
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force_marlin = get_bool_env_var("SGLANG_FORCE_FP8_MARLIN")
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auto_enable = can_auto_enable_marlin_fp8()
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self.use_marlin = force_marlin or auto_enable
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self.block_quant = self.quant_config.weight_block_size is not None
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self.w8a8_block_fp8_linear = dispatch_w8a8_block_fp8_linear()
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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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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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tp_size = get_tensor_model_parallel_world_size()
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if self.block_quant:
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block_n, block_k = (
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self.quant_config.weight_block_size[0],
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self.quant_config.weight_block_size[1],
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)
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# Required by row parallel
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if tp_size > 1 and input_size // input_size_per_partition == tp_size:
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if input_size_per_partition % block_k != 0:
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raise ValueError(
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f"Weight input_size_per_partition = "
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f"{input_size_per_partition} is not divisible by "
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f"weight quantization block_k = {block_k}."
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)
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# Required by column parallel or enabling merged weights
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if (
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tp_size > 1 and output_size // output_size_per_partition == tp_size
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) or len(output_partition_sizes) > 1:
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for output_partition_size in output_partition_sizes:
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if output_partition_size % block_n != 0:
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raise ValueError(
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f"Weight output_partition_size = "
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f"{output_partition_size} is not divisible by "
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f"weight quantization block_n = {block_n}."
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)
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layer.logical_widths = output_partition_sizes
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layer.input_size_per_partition = input_size_per_partition
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layer.output_size_per_partition = output_size_per_partition
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layer.orig_dtype = params_dtype
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# WEIGHT
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weight_dtype = (
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torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_serialized
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else params_dtype
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)
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weight = ModelWeightParameter(
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data=torch.empty(
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output_size_per_partition, input_size_per_partition, 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 checkpoint is serialized fp8, load them.
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# Otherwise, wait until process_weights_after_loading.
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if self.quant_config.is_checkpoint_fp8_serialized:
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# WEIGHT SCALE
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if self.block_quant:
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if hasattr(self.quant_config, "activation_scheme"):
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assert self.quant_config.activation_scheme == "dynamic"
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elif hasattr(self.quant_config, "linear_activation_scheme"):
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assert self.quant_config.linear_activation_scheme == "dynamic"
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scale = BlockQuantScaleParameter(
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data=torch.empty(
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(output_size_per_partition + block_n - 1) // block_n,
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(input_size_per_partition + block_k - 1) // block_k,
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dtype=torch.float32,
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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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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("weight_scale_inv", scale)
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else:
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scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader,
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)
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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("weight_scale", scale)
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# INPUT ACTIVATION SCALE
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if (
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hasattr(self.quant_config, "activation_scheme")
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and self.quant_config.activation_scheme == "static"
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) or (
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hasattr(self.quant_config, "linear_activation_scheme")
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and self.quant_config.linear_activation_scheme == "static"
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):
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scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader,
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)
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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("input_scale", scale)
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else:
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layer.register_parameter("input_scale", None)
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def process_weights_after_loading(self, layer: Module) -> None:
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if self.block_quant:
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# If ROCm, normalize the weights and scales to e4m3fnuz
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if _is_fp8_fnuz:
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# activation_scheme: dynamic
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weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
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weight=layer.weight,
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weight_scale=layer.weight_scale_inv,
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input_scale=None,
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)
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layer.input_scale = None
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elif _is_cpu:
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assert (
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_is_cpu_amx_available
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), "Fp8LinearMethod on CPU requires that CPU has AMX support"
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_amx_process_weight_after_loading(layer, ["weight"])
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return
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else:
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weight, weight_scale = layer.weight.data, layer.weight_scale_inv.data
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layer.weight = Parameter(weight, requires_grad=False)
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layer.weight_scale_inv = Parameter(weight_scale, requires_grad=False)
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else:
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layer.weight = Parameter(layer.weight.data, requires_grad=False)
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# If checkpoint not serialized fp8, quantize the weights.
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if not self.quant_config.is_checkpoint_fp8_serialized:
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if self.cutlass_fp8_supported or self.use_marlin:
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# apply per-channel quantization default as
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# cutlass sgl-kernel and marlin only support per-channel scale
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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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# per-tensor quantization
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qweight, weight_scale = input_to_float8(layer.weight)
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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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# If checkpoint is fp8, handle that there are N scales for N
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# shards in a fused module
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else:
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layer.weight_scale = Parameter(
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layer.weight_scale.data, requires_grad=False
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)
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if (
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hasattr(self.quant_config, "activation_scheme")
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and self.quant_config.activation_scheme == "static"
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) or (
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hasattr(self.quant_config, "linear_activation_scheme")
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and self.quant_config.linear_activation_scheme == "static"
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):
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layer.input_scale = Parameter(
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layer.input_scale.data, requires_grad=False
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)
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# cutlass sgl-kernel and marlin only support per-channel scale
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if self.cutlass_fp8_supported or self.use_marlin:
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weight = layer.weight
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weight_scale = convert_to_channelwise(
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layer.weight_scale, layer.logical_widths
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)
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else:
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# Dequant -> Quant with max scale so we can run per tensor.
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weight = layer.weight
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weight_scale = layer.weight_scale
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# If ROCm, normalize the weights and scales to e4m3fnuz
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if _is_fp8_fnuz:
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weight, weight_scale, input_scale = (
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normalize_e4m3fn_to_e4m3fnuz(
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weight=weight,
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weight_scale=weight_scale,
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input_scale=layer.input_scale,
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)
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)
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if input_scale is not None:
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layer.input_scale = Parameter(
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input_scale, requires_grad=False
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)
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weight_scale, weight = requantize_with_max_scale(
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weight=weight,
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weight_scale=weight_scale,
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logical_widths=layer.logical_widths,
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)
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# Update layer with new values.
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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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if (
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hasattr(self.quant_config, "activation_scheme")
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and self.quant_config.activation_scheme == "static"
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) or (
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hasattr(self.quant_config, "linear_activation_scheme")
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and self.quant_config.linear_activation_scheme == "static"
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):
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layer.input_scale = Parameter(
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layer.input_scale.max(), requires_grad=False
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)
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if self.use_marlin:
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if self.block_quant:
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layer.weight_block_size = self.quant_config.weight_block_size
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prepare_fp8_layer_for_marlin(layer, not self.block_quant)
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# Activations not quantized for marlin.
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del layer.input_scale
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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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) -> torch.Tensor:
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if self.use_marlin:
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return apply_fp8_marlin_linear(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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workspace=layer.workspace,
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size_n=layer.output_size_per_partition,
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size_k=layer.input_size_per_partition,
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bias=bias,
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)
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if self.block_quant:
|
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if use_intel_amx_backend(layer):
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return torch.ops.sgl_kernel.fp8_scaled_mm_cpu(
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x,
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layer.weight,
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layer.weight_scale_inv,
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self.quant_config.weight_block_size,
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bias,
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x.dtype,
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True, # is_vnni
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)
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|
return self.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=None,
|
|
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,
|
|
)
|
|
|
|
|
|
def get_tile_tokens_dim(num_tokens, top_k, num_experts):
|
|
# Guess tokens per expert assuming perfect expert distribution first.
|
|
num_tokens_per_expert = (num_tokens * top_k) // num_experts
|
|
# And pad the number to the next power of 2.
|
|
tile_tokens_dim = next_power_of_2(num_tokens_per_expert)
|
|
# Cap to 8-64 tokens per CTA tile as it's the range supported by the kernel.
|
|
tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
|
|
return tile_tokens_dim
|
|
|
|
|
|
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
|
|
self.block_quant = self.quant_config.weight_block_size is not None
|
|
self.cutlass_fp8_supported = cutlass_fp8_supported()
|
|
self.use_cutlass_fused_experts_fp8 = (
|
|
get_bool_env_var("SGLANG_CUTLASS_MOE")
|
|
and self.cutlass_fp8_supported
|
|
and self.block_quant
|
|
and (is_sm100_supported() or is_sm90_supported())
|
|
)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: Module,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
|
|
|
|
if self.quant_config.is_checkpoint_fp8_serialized:
|
|
params_dtype = torch.uint32 if _use_hip_int4 else torch.float8_e4m3fn
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
if self.block_quant:
|
|
block_n, block_k = (
|
|
self.quant_config.weight_block_size[0],
|
|
self.quant_config.weight_block_size[1],
|
|
)
|
|
# NOTE(HandH1998): To ensure proper alignment of the block-wise quantization scales, the output_size of the weights for both the gate and up layers must be divisible by block_n.
|
|
# Required by column parallel or enabling merged weights
|
|
if intermediate_size_per_partition % block_n != 0:
|
|
raise ValueError(
|
|
f"The output_size of gate's and up's weight = "
|
|
f"{intermediate_size_per_partition} is not divisible by "
|
|
f"weight quantization block_n = {block_n}."
|
|
)
|
|
if tp_size > 1:
|
|
# Required by row parallel
|
|
if intermediate_size_per_partition % block_k != 0:
|
|
raise ValueError(
|
|
f"The input_size of down's weight = "
|
|
f"{intermediate_size_per_partition} is not divisible by "
|
|
f"weight quantization block_k = {block_k}."
|
|
)
|
|
|
|
# WEIGHTS
|
|
if _is_hip and _use_hip_int4:
|
|
# INT4 MoE weight - INT32 packed
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
hidden_size // 8,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition // 8,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
else:
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
hidden_size,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
if self.block_quant:
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * ((intermediate_size_per_partition + block_n - 1) // block_n),
|
|
(hidden_size + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
(hidden_size + block_n - 1) // block_n,
|
|
(intermediate_size_per_partition + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)
|
|
assert self.quant_config.activation_scheme == "dynamic"
|
|
if self.use_cutlass_fused_experts_fp8:
|
|
self.ab_strides1 = torch.full(
|
|
(num_experts,),
|
|
hidden_size,
|
|
device=w13_weight.device,
|
|
dtype=torch.int64,
|
|
)
|
|
self.c_strides1 = torch.full(
|
|
(num_experts,),
|
|
2 * intermediate_size_per_partition,
|
|
device=w13_weight.device,
|
|
dtype=torch.int64,
|
|
)
|
|
self.ab_strides2 = torch.full(
|
|
(num_experts,),
|
|
intermediate_size_per_partition,
|
|
device=w2_weight.device,
|
|
dtype=torch.int64,
|
|
)
|
|
self.c_strides2 = torch.full(
|
|
(num_experts,),
|
|
hidden_size,
|
|
device=w2_weight.device,
|
|
dtype=torch.int64,
|
|
)
|
|
self.workspace = torch.empty(
|
|
90000, device=w13_weight.device, dtype=torch.uint8
|
|
)
|
|
self.a_ptr = torch.empty(
|
|
num_experts, device=w13_weight.device, dtype=torch.int64
|
|
)
|
|
self.b_ptr = torch.empty(
|
|
num_experts, device=w13_weight.device, dtype=torch.int64
|
|
)
|
|
self.out_ptr = torch.empty(
|
|
num_experts, device=w13_weight.device, dtype=torch.int64
|
|
)
|
|
self.a_scales_ptr = torch.empty(
|
|
num_experts, device=w13_weight.device, dtype=torch.int64
|
|
)
|
|
self.b_scales_ptr = torch.empty(
|
|
num_experts, device=w13_weight.device, dtype=torch.int64
|
|
)
|
|
self.expert_offsets = torch.empty(
|
|
num_experts + 1, device=w13_weight.device, dtype=torch.int32
|
|
)
|
|
self.problem_sizes1 = torch.empty(
|
|
num_experts, 3, device=w13_weight.device, dtype=torch.int32
|
|
)
|
|
self.problem_sizes2 = torch.empty(
|
|
num_experts, 3, device=w13_weight.device, dtype=torch.int32
|
|
)
|
|
|
|
else:
|
|
# 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
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32), requires_grad=False
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
|
|
if _is_hip: # _use_aiter: TODO: add check back after triton kernel
|
|
# ROCm - using column scaling, duplicate scaling numbers in case per tensor scaling
|
|
w13_weight_scale1 = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale1 = torch.nn.Parameter(
|
|
torch.ones(num_experts, hidden_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale1", w13_weight_scale1)
|
|
layer.register_parameter("w2_weight_scale1", w2_weight_scale1)
|
|
|
|
# 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.BLOCK.value}
|
|
if self.block_quant
|
|
else {"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)
|
|
|
|
if _is_hip and _use_hip_int4:
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale1, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale1, 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 _is_hip and _use_hip_int4:
|
|
self.process_weights_hip_int4(layer)
|
|
return
|
|
|
|
# Block quant doesn't need to process weights after loading
|
|
if self.block_quant:
|
|
# If ROCm, normalize the weights and scales to e4m3fnuz
|
|
if _is_fp8_fnuz:
|
|
# activation_scheme: dynamic
|
|
w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
|
|
weight=layer.w13_weight,
|
|
weight_scale=layer.w13_weight_scale_inv,
|
|
input_scale=None,
|
|
)
|
|
w2_weight, w2_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
|
|
weight=layer.w2_weight,
|
|
weight_scale=layer.w2_weight_scale_inv,
|
|
input_scale=None,
|
|
)
|
|
# Reset the parameter
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
|
|
layer.w13_weight_scale_inv = torch.nn.Parameter(
|
|
w13_weight_scale, requires_grad=False
|
|
)
|
|
layer.w13_input_scale = None
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
|
|
layer.w2_weight_scale_inv = torch.nn.Parameter(
|
|
w2_weight_scale, requires_grad=False
|
|
)
|
|
layer.w2_input_scale = None
|
|
|
|
if _use_aiter:
|
|
# Pre-shuffle weights
|
|
layer.w13_weight.data = shuffle_weight(
|
|
layer.w13_weight.contiguous(), (16, 16)
|
|
)
|
|
layer.w2_weight.data = shuffle_weight(
|
|
layer.w2_weight.contiguous(), (16, 16)
|
|
)
|
|
|
|
if _is_cpu:
|
|
assert (
|
|
_is_cpu_amx_available
|
|
), "Fp8MoEMethod on CPU requires that CPU has AMX support"
|
|
_amx_process_weight_after_loading(layer, ["w13_weight", "w2_weight"])
|
|
|
|
return
|
|
|
|
# If checkpoint is fp16 or bfloat16, quantize in place.
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
# If ROCm, fp8_dtype will be float8_e4m3fnuz (MI300x HW)
|
|
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_local_experts,
|
|
dtype=torch.float32,
|
|
device=w13_weight.device,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
for expert in range(layer.num_local_experts):
|
|
w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
|
|
scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
|
|
)
|
|
w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
|
|
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)
|
|
|
|
if _is_hip:
|
|
self.process_weights_hip_scale_padding(layer)
|
|
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_fp8_fnuz:
|
|
# 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_local_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, :],
|
|
_,
|
|
) = 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
|
|
)
|
|
|
|
if _is_hip:
|
|
self.process_weights_hip_scale_padding(layer)
|
|
return
|
|
|
|
def process_weights_hip_int4(self, layer: Module):
|
|
# TODO: _use_aiter: add after triton kernel added
|
|
# INT4-FP8 (INT4 MoE Weight, FP8 Compute)
|
|
# Weight Permutation
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
shuffle_weight(layer.w13_weight.data, (16, 16)),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
shuffle_weight(layer.w2_weight.data, (16, 16)),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
# INT4-FP8 : offset INT4 w13_weight_scale1 to single w13_weight_scale
|
|
# Fp8 moe kernel needs single fp8 w13_weight_scale for w13 per expert.
|
|
# We won't do requant each expert's fp8 weight (not direct available),
|
|
# instead we adjust half of INT4 w13_weight_scale1 numbers
|
|
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_local_experts):
|
|
start = 0
|
|
max_w13_scale_fp8 = max_w13_scales[expert_id]
|
|
for shard_id in range(2):
|
|
if layer.w13_weight_scale[expert_id][shard_id] != max_w13_scale_fp8:
|
|
int4_rescale = (
|
|
layer.w13_weight_scale[expert_id][shard_id] / max_w13_scale_fp8
|
|
)
|
|
layer.w13_weight_scale1[expert_id][
|
|
start : start + shard_size
|
|
] *= int4_rescale
|
|
start += shard_size
|
|
|
|
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False)
|
|
|
|
# special hack to asm_moe, which takes (weight_scale1 * weight_scale) as post GEMM scaling
|
|
# optimal design - shall apply per-column weight_scale1 before GEMM, and weight_scale post
|
|
for expert_id in range(layer.num_local_experts):
|
|
layer.w13_weight_scale1[expert_id] *= max_w13_scales[expert_id]
|
|
layer.w2_weight_scale1[expert_id] *= layer.w2_weight_scale[expert_id]
|
|
|
|
def process_weights_hip_scale_padding(self, layer: Module):
|
|
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
|
|
padding_size, # Avoid circular import
|
|
)
|
|
|
|
if _use_aiter:
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
shuffle_weight(layer.w13_weight.data, (16, 16)),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
shuffle_weight(layer.w2_weight.data, (16, 16)),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
# ROCm (_use_aiter): using column-wise scaling
|
|
layer.w13_weight_scale1 *= layer.w13_weight_scale.unsqueeze(-1)
|
|
layer.w2_weight_scale1 *= layer.w2_weight_scale.unsqueeze(-1)
|
|
elif get_bool_env_var("SGLANG_MOE_PADDING"):
|
|
# If ROCm, apply weight padding (min. Mem channel contention) only if set
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
F.pad(layer.w2_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
def create_moe_runner(
|
|
self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
|
|
):
|
|
self.moe_runner_config = moe_runner_config
|
|
self.runner = MoeRunner(MoeRunnerBackend.TRITON, moe_runner_config)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
dispatch_output: DispatchOutput,
|
|
) -> CombineInput:
|
|
|
|
from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
|
|
|
|
x = dispatch_output.hidden_states
|
|
topk_output = dispatch_output.topk_output
|
|
moe_runner_config = self.moe_runner_config
|
|
|
|
if use_intel_amx_backend(layer):
|
|
from sglang.srt.layers.moe.topk import apply_topk_weights_cpu
|
|
|
|
topk_weights, topk_ids, _ = topk_output
|
|
x, topk_weights = apply_topk_weights_cpu(
|
|
moe_runner_config.apply_router_weight_on_input, topk_weights, x
|
|
)
|
|
|
|
output = torch.ops.sgl_kernel.fused_experts_cpu(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
False, # inplace See [Note] inplace should be False in fused_experts.
|
|
False, # use_int8_w8a8
|
|
True, # use_fp8_w8a16
|
|
layer.w13_weight_scale_inv, # w1_scale
|
|
layer.w2_weight_scale_inv, # w2_scale
|
|
self.quant_config.weight_block_size, # block_size
|
|
None, # a1_scale
|
|
None, # a2_scale
|
|
True, # is_vnni
|
|
)
|
|
return StandardCombineInput(hidden_states=output)
|
|
|
|
if _is_hip:
|
|
ret = self.maybe_apply_hip_fused_experts(
|
|
layer,
|
|
x,
|
|
topk_output,
|
|
moe_runner_config.activation,
|
|
moe_runner_config.no_combine,
|
|
)
|
|
if ret is not None:
|
|
return StandardCombineInput(hidden_states=ret)
|
|
|
|
if self.use_cutlass_fused_experts_fp8:
|
|
from sglang.srt.layers.moe.cutlass_moe import cutlass_fused_experts_fp8
|
|
|
|
topk_weights, topk_ids, _ = topk_output
|
|
output = cutlass_fused_experts_fp8(
|
|
x,
|
|
layer.w13_weight.transpose(1, 2),
|
|
layer.w2_weight.transpose(1, 2),
|
|
layer.w13_weight_scale_inv.transpose(1, 2),
|
|
layer.w2_weight_scale_inv.transpose(1, 2),
|
|
topk_weights,
|
|
topk_ids,
|
|
self.ab_strides1,
|
|
self.c_strides1,
|
|
self.ab_strides2,
|
|
self.c_strides2,
|
|
self.workspace,
|
|
self.a_ptr,
|
|
self.b_ptr,
|
|
self.out_ptr,
|
|
self.a_scales_ptr,
|
|
self.b_scales_ptr,
|
|
self.expert_offsets,
|
|
self.problem_sizes1,
|
|
self.problem_sizes2,
|
|
use_fp8_blockscale=True,
|
|
)
|
|
return StandardCombineInput(hidden_states=output)
|
|
|
|
quant_info = TritonMoeQuantInfo(
|
|
w13_weight=layer.w13_weight,
|
|
w2_weight=layer.w2_weight,
|
|
use_fp8_w8a8=True,
|
|
w13_scale=(
|
|
layer.w13_weight_scale_inv
|
|
if self.block_quant
|
|
else layer.w13_weight_scale
|
|
),
|
|
w2_scale=(
|
|
layer.w2_weight_scale_inv if self.block_quant else layer.w2_weight_scale
|
|
),
|
|
a13_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
)
|
|
return self.runner.run(dispatch_output, quant_info)
|
|
|
|
def apply_with_router_logits(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
dispatch_output: StandardDispatchOutput,
|
|
) -> torch.Tensor:
|
|
x = dispatch_output.hidden_states
|
|
topk_output = dispatch_output.topk_output
|
|
|
|
activation = self.moe_runner_config.activation
|
|
routed_scaling_factor = self.moe_runner_config.routed_scaling_factor
|
|
|
|
from flashinfer.fused_moe import trtllm_fp8_block_scale_moe
|
|
|
|
from sglang.srt.layers.moe.topk import TopKOutputChecker
|
|
|
|
assert TopKOutputChecker.format_is_bypassed(topk_output)
|
|
router_logits = topk_output.router_logits
|
|
topk_config = topk_output.topk_config
|
|
assert (
|
|
activation == "silu"
|
|
), "Only silu is supported for flashinfer blockscale fp8 moe"
|
|
a_q, a_sf = per_token_group_quant_fp8(x, self.quant_config.weight_block_size[1])
|
|
# NOTE: scales of hidden states have to be transposed!
|
|
a_sf_t = a_sf.t().contiguous()
|
|
|
|
assert (
|
|
topk_config.num_expert_group is not None
|
|
and topk_config.topk_group is not None
|
|
), "Current trtllm_fp8_block_scale_moe kernel does not support these two arguments as None"
|
|
|
|
correction_bias = (
|
|
None
|
|
if topk_config.correction_bias is None
|
|
else topk_config.correction_bias.to(x.dtype)
|
|
)
|
|
|
|
return trtllm_fp8_block_scale_moe(
|
|
routing_logits=router_logits.to(torch.float32),
|
|
routing_bias=correction_bias,
|
|
hidden_states=a_q,
|
|
hidden_states_scale=a_sf_t,
|
|
gemm1_weights=layer.w13_weight,
|
|
gemm1_weights_scale=layer.w13_weight_scale_inv,
|
|
gemm2_weights=layer.w2_weight,
|
|
gemm2_weights_scale=layer.w2_weight_scale_inv,
|
|
num_experts=layer.num_experts,
|
|
top_k=topk_config.top_k,
|
|
n_group=topk_config.num_expert_group,
|
|
topk_group=topk_config.topk_group,
|
|
intermediate_size=layer.w2_weight.shape[2],
|
|
local_expert_offset=layer.moe_ep_rank * layer.num_local_experts,
|
|
local_num_experts=layer.num_local_experts,
|
|
routed_scaling_factor=(
|
|
routed_scaling_factor if routed_scaling_factor is not None else 1.0
|
|
),
|
|
tile_tokens_dim=get_tile_tokens_dim(
|
|
x.shape[0], topk_config.top_k, layer.num_experts
|
|
),
|
|
routing_method_type=2, # DeepSeek-styled routing method
|
|
use_shuffled_weight=False,
|
|
)
|
|
|
|
def maybe_apply_hip_fused_experts(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
topk_output: TopKOutput,
|
|
activation: str = "silu",
|
|
no_combine: bool = False,
|
|
) -> Optional[torch.Tensor]:
|
|
topk_weights, topk_ids, _ = topk_output
|
|
if _use_hip_int4:
|
|
# TODO: add triton kernel and add check _use_aiter
|
|
assert not no_combine, f"{no_combine=} is not supported."
|
|
return fused_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
quant_type=QuantType.per_Token,
|
|
w1_scale=layer.w13_weight_scale1,
|
|
w2_scale=layer.w2_weight_scale1,
|
|
activation=(
|
|
ActivationType.Silu if activation == "silu" else ActivationType.Gelu
|
|
),
|
|
)
|
|
|
|
if _use_aiter:
|
|
assert not no_combine, f"{no_combine=} is not supported."
|
|
if self.block_quant:
|
|
return fused_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
w1_scale=layer.w13_weight_scale_inv,
|
|
w2_scale=layer.w2_weight_scale_inv,
|
|
quant_type=QuantType.per_128x128,
|
|
activation=(
|
|
ActivationType.Silu
|
|
if activation == "silu"
|
|
else ActivationType.Gelu
|
|
),
|
|
expert_mask=None,
|
|
)
|
|
else:
|
|
return fused_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
quant_type=QuantType.per_Token,
|
|
w1_scale=layer.w13_weight_scale1,
|
|
w2_scale=layer.w2_weight_scale1,
|
|
activation=(
|
|
ActivationType.Silu
|
|
if activation == "silu"
|
|
else ActivationType.Gelu
|
|
),
|
|
)
|
|
return None
|
|
|
|
|
|
class Fp8KVCacheMethod(BaseKVCacheMethod):
|
|
"""
|
|
Supports loading kv-cache scaling factors from FP8 checkpoints.
|
|
"""
|
|
|
|
def __init__(self, quant_config: Fp8Config):
|
|
super().__init__(quant_config)
|