1134 lines
52 KiB
Python
1134 lines
52 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import TYPE_CHECKING, Any, Callable, Optional
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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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import vllm.envs as envs
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import vllm.model_executor.layers.fused_moe.modular_kernel as mk
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from vllm import _custom_ops as ops
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe import (
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FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase,
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FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize,
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FusedMoeWeightScaleSupported)
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from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
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UnquantizedLinearMethod)
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
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from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
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FlashinferMoeBackend, apply_flashinfer_per_tensor_scale_fp8,
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build_flashinfer_fp8_cutlass_moe_prepare_finalize,
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flashinfer_cutlass_moe_fp8, get_flashinfer_moe_backend,
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register_moe_scaling_factors, rotate_flashinfer_fp8_moe_weights,
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select_cutlass_fp8_gemm_impl, swap_w13_to_w31)
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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get_col_major_tma_aligned_tensor, requant_weight_ue8m0_inplace)
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
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apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin,
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prepare_moe_fp8_layer_for_marlin)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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GroupShape, is_layer_skipped)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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Fp8LinearOp, all_close_1d, cutlass_block_fp8_supported,
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cutlass_fp8_supported, maybe_create_device_identity,
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normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
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requantize_with_max_scale)
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from vllm.model_executor.parameter import (BlockQuantScaleParameter,
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ModelWeightParameter,
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PerTensorScaleParameter)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.scalar_type import scalar_types
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from vllm.utils import has_deep_gemm
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from vllm.utils.deep_gemm import (is_blackwell_deep_gemm_e8m0_used,
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is_deep_gemm_supported)
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from vllm.utils.flashinfer import has_flashinfer_moe
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if TYPE_CHECKING:
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from vllm.model_executor.models.utils import WeightsMapper
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ACTIVATION_SCHEMES = ["static", "dynamic"]
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logger = init_logger(__name__)
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def _is_col_major(x: torch.Tensor) -> bool:
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assert x.dim() == 3
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b, m, n = x.shape
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return x.stride(0) == m * n and x.stride(1) == 1 and x.stride(2) == m
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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: Optional[list[int]] = None,
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) -> None:
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super().__init__()
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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if activation_scheme not in ACTIVATION_SCHEMES:
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raise ValueError(
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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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"The block-wise quantization only supports fp8-serialized "
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"checkpoint for now.")
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if len(weight_block_size) != 2:
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raise ValueError(
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"The quantization block size of weight must have 2 "
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f"dimensions, but got {len(weight_block_size)} dimensions")
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if activation_scheme != "dynamic":
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raise ValueError("The block-wise quantization only supports "
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"dynamic activation scheme for now, but got "
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f"{activation_scheme} activation scheme.")
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self.weight_block_size = weight_block_size
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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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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def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
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if self.ignored_layers is not None:
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self.ignored_layers = hf_to_vllm_mapper.apply_list(
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self.ignored_layers)
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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"],
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None)
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if not ignored_layers:
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ignored_layers = cls.get_from_keys_or(config,
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["modules_to_not_convert"],
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None)
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return cls(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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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QuantizeMethodBase"]:
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from vllm.attention.layer import Attention # Avoid circular import
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if isinstance(layer, LinearBase):
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if is_layer_skipped(prefix=prefix,
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ignored_layers=self.ignored_layers,
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fused_mapping=self.packed_modules_mapping):
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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, layer)
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elif isinstance(layer, Attention):
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return Fp8KVCacheMethod(self)
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return None
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def get_cache_scale(self, name: str) -> Optional[str]:
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"""
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Check whether the param name matches the format for k/v cache scales
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in compressed-tensors. If this is the case, return its equivalent
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param name expected by vLLM
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:param name: param name
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:return: matching param name for KV cache scale in vLLM
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"""
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if name.endswith(".output_scale") and ".k_proj" in name:
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return name.replace(".k_proj.output_scale", ".attn.k_scale")
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if name.endswith(".output_scale") and ".v_proj" in name:
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return name.replace(".v_proj.output_scale", ".attn.v_scale")
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if name.endswith(".output_scale") and ".q_proj" in name:
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return name.replace(".q_proj.output_scale", ".attn.q_scale")
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if name.endswith("self_attn.prob_output_scale"):
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return name.replace(".prob_output_scale", ".attn.prob_scale")
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# If no matches, return None
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return None
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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: Fp8Config):
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self.quant_config = quant_config
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self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
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self.out_dtype = torch.get_default_dtype()
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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 = (not current_platform.has_device_capability(89)
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or envs.VLLM_TEST_FORCE_FP8_MARLIN)
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# Disable marlin for rocm
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if current_platform.is_rocm():
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self.use_marlin = False
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# AITER is only supported on ROCm and only for FP8_FNUZ
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# and at the moment are MI300 series
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self.use_aiter_and_is_supported = (current_platform.is_rocm()
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and envs.VLLM_ROCM_USE_AITER
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and envs.VLLM_ROCM_USE_AITER_LINEAR
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and current_platform.is_fp8_fnuz())
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self.block_quant = self.quant_config.weight_block_size is not None
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self.act_q_static = self.quant_config.activation_scheme == "static"
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# Use per-token quantization for better perf if dynamic and cutlass
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if not self.act_q_static and cutlass_fp8_supported():
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self.act_q_group_shape = GroupShape.PER_TOKEN
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else:
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self.act_q_group_shape = GroupShape.PER_TENSOR
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self.fp8_linear = Fp8LinearOp(
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act_quant_static=self.act_q_static,
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act_quant_group_shape=self.act_q_group_shape,
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cutlass_fp8_supported=cutlass_fp8_supported())
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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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maybe_create_device_identity()
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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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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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layer.weight_block_size = None
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if self.block_quant:
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tp_size = get_tensor_model_parallel_world_size()
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assert self.quant_config.weight_block_size is not None
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layer.weight_block_size = self.quant_config.weight_block_size
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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
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and input_size // input_size_per_partition == tp_size
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and 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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# Required by column parallel or enabling merged weights
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is_tp_split = (tp_size > 1 and
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output_size // output_size_per_partition == tp_size)
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is_merged_gemm = len(output_partition_sizes) > 1
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if is_tp_split or is_merged_gemm:
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sizes_to_check = output_partition_sizes
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if not is_tp_split and is_merged_gemm:
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# In case of merged matrices, we allow the last
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# matrix to not be a multiple of block size
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sizes_to_check = output_partition_sizes[:-1]
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for output_partition_size in sizes_to_check:
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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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# WEIGHT
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weight_dtype = (torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_serialized else
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params_dtype)
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weight = ModelWeightParameter(data=torch.empty(
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output_size_per_partition,
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input_size_per_partition,
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dtype=weight_dtype),
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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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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 not self.block_quant:
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scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes),
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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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set_weight_attrs(scale, {"scale_type": "weight_scale"})
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layer.register_parameter("weight_scale", scale)
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else:
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assert self.quant_config.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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set_weight_attrs(scale, {"scale_type": "weight_scale"})
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# The weight_scale_inv name is intentional for deepseekv3
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layer.register_parameter("weight_scale_inv", scale)
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# INPUT ACTIVATION SCALE
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if self.quant_config.activation_scheme == "static":
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scale = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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scale[:] = torch.finfo(torch.float32).min
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set_weight_attrs(scale, {"scale_type": "input_scale"})
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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 _maybe_pad_weight(self, weight: torch.Tensor) -> torch.Tensor:
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# Pad the weight tensor. This is an optimization on ROCm platform, which
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# can benefit from tensors located far enough from one another in memory
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if (envs.VLLM_ROCM_FP8_PADDING and current_platform.is_rocm()
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and weight.stride(-1) == 1
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and (weight.stride(-2) * weight.element_size()) % 512 == 0):
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num_pad = 256 // weight.element_size()
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weight = F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
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torch.cuda.empty_cache()
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return weight
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def process_weights_after_loading(self, layer: Module) -> None:
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size_k_first = True
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# TODO(rob): refactor block quant into separate class.
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if self.block_quant:
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assert self.quant_config.activation_scheme == "dynamic"
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size_k_first = False
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if current_platform.is_fp8_fnuz():
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weight, weight_scale_inv, _ = \
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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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else:
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weight = layer.weight.data
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weight_scale_inv = layer.weight_scale_inv.data
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weight = self._maybe_pad_weight(weight)
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# Torch.compile cannot use Parameter subclasses.
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layer.weight = Parameter(weight, requires_grad=False)
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layer.weight_scale_inv = Parameter(weight_scale_inv,
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requires_grad=False)
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# If checkpoint not serialized fp8, quantize the weights.
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elif not self.quant_config.is_checkpoint_fp8_serialized:
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qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
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scale=None)
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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 = torch.nn.Parameter(layer.weight_scale.data,
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requires_grad=False)
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if self.quant_config.activation_scheme == "static":
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layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
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requires_grad=False)
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weight = layer.weight
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weight_scale = layer.weight_scale
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# If using w8a8, torch._scaled_mm needs per tensor, so
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# requantize the logical shards as a single weight.
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if not self.use_marlin:
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# Dequant -> Quant with max scale so we can run per tensor.
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if current_platform.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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if input_scale is not None:
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layer.input_scale = Parameter(input_scale,
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requires_grad=False)
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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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weight = self._maybe_pad_weight(weight)
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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 self.quant_config.activation_scheme == "static":
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layer.input_scale = Parameter(layer.input_scale.max(),
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requires_grad=False)
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if self.use_marlin:
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prepare_fp8_layer_for_marlin(layer, size_k_first)
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# Activations not quantized for marlin.
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del layer.input_scale
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# On B200, if E8M0 for DeepGemm is used, we need to
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# requantize the weight and input to the specific scale
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# at the same time.
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if is_blackwell_deep_gemm_e8m0_used():
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assert layer.weight_block_size is not None
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block_sz = tuple(layer.weight_block_size)
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requant_weight_ue8m0_inplace(
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layer.weight.data,
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layer.weight_scale_inv.data if hasattr(
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layer, "weight_scale_inv") else layer.weight_scale.data,
|
|
block_sz,
|
|
)
|
|
|
|
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)
|
|
|
|
if self.block_quant:
|
|
assert self.quant_config.weight_block_size is not None
|
|
|
|
return torch.ops.vllm.apply_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=layer.input_scale,
|
|
bias=bias,
|
|
cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
|
|
use_aiter_and_is_supported=self.use_aiter_and_is_supported,
|
|
)
|
|
|
|
return self.fp8_linear.apply(input=x,
|
|
weight=layer.weight,
|
|
weight_scale=layer.weight_scale,
|
|
out_dtype=self.out_dtype,
|
|
input_scale=layer.input_scale,
|
|
bias=bias)
|
|
|
|
|
|
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, layer: torch.nn.Module):
|
|
super().__init__(layer.moe_config)
|
|
self.layer = layer
|
|
self.quant_config = quant_config
|
|
self.block_quant = self.quant_config.weight_block_size is not None
|
|
|
|
self.flashinfer_moe_backend: Optional[FlashinferMoeBackend] = None
|
|
self.fused_experts: Optional[
|
|
mk.FusedMoEModularKernel] = None # type: ignore
|
|
if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
|
|
self.flashinfer_moe_backend = get_flashinfer_moe_backend()
|
|
logger.info_once(
|
|
f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
|
|
)
|
|
# For GPUs that lack FP8 hardware support, we can leverage the Marlin
|
|
# kernel for fast weight-only FP8 quantization
|
|
self.use_marlin = (not current_platform.has_device_capability(89)
|
|
or envs.VLLM_TEST_FORCE_FP8_MARLIN)
|
|
# Disable marlin for rocm
|
|
if current_platform.is_rocm():
|
|
self.use_marlin = False
|
|
|
|
# Check for DeepGemm support.
|
|
self.allow_deep_gemm = False
|
|
if envs.VLLM_USE_DEEP_GEMM:
|
|
if not has_deep_gemm():
|
|
logger.warning_once("Failed to import DeepGemm kernels.")
|
|
elif not self.block_quant:
|
|
logger.warning_once("Model is not block quantized. Not using "
|
|
"DeepGemm kernels")
|
|
elif (is_deep_gemm_supported()):
|
|
logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.")
|
|
self.allow_deep_gemm = True
|
|
else:
|
|
logger.warning_once(
|
|
"DeepGemm not supported on the current platform.")
|
|
|
|
# Check for CutlassBlockScaledGroupedGemm support.
|
|
self.allow_cutlass_block_scaled_grouped_gemm = False
|
|
if not self.block_quant:
|
|
logger.debug_once("Model is not block quantized. Not using "
|
|
"CutlassBlockScaledGroupedGemm kernels")
|
|
elif (current_platform.is_cuda()
|
|
and current_platform.is_device_capability(100)):
|
|
logger.info_once(
|
|
"Using CutlassBlockScaledGroupedGemm kernels for Fp8MoEMethod."
|
|
)
|
|
self.allow_cutlass_block_scaled_grouped_gemm = True
|
|
else:
|
|
logger.warning_once(
|
|
"CutlassBlockScaledGroupedGemm not supported on the current "
|
|
"platform.")
|
|
|
|
def maybe_make_prepare_finalize(
|
|
self,
|
|
moe: FusedMoEConfig,
|
|
) -> Optional[mk.FusedMoEPrepareAndFinalize]:
|
|
if self.flashinfer_moe_backend != FlashinferMoeBackend.CUTLASS:
|
|
return super().maybe_make_prepare_finalize(moe)
|
|
|
|
prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
|
|
moe,
|
|
layer=self.layer,
|
|
)
|
|
logger.debug_once("%s", prepare_finalize.__class__.__name__)
|
|
return prepare_finalize
|
|
|
|
def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype, **extra_weight_attrs):
|
|
|
|
layer.intermediate_size_per_partition = intermediate_size_per_partition
|
|
layer.hidden_size = hidden_size
|
|
layer.num_experts = num_experts
|
|
layer.orig_dtype = params_dtype
|
|
layer.weight_block_size = None
|
|
|
|
if self.quant_config.is_checkpoint_fp8_serialized:
|
|
params_dtype = torch.float8_e4m3fn
|
|
if self.block_quant:
|
|
assert self.quant_config.weight_block_size is not None
|
|
layer.weight_block_size = self.quant_config.weight_block_size
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
block_n, block_k = (
|
|
self.quant_config.weight_block_size[0],
|
|
self.quant_config.weight_block_size[1],
|
|
)
|
|
# NOTE: 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
|
|
and intermediate_size_per_partition % block_k != 0):
|
|
# Required by row parallel
|
|
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
|
|
w13_weight = torch.nn.Parameter(torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
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_per_partition,
|
|
dtype=params_dtype),
|
|
requires_grad=False)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
if not self.block_quant:
|
|
# 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)
|
|
else:
|
|
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"
|
|
|
|
# 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)
|
|
|
|
# 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:
|
|
# Lazy import to avoid importing triton too early.
|
|
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
|
|
is_rocm_aiter_moe_enabled, shuffle_weights)
|
|
|
|
self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled()
|
|
|
|
# TODO (rob): refactor block quant into separate class.
|
|
if self.block_quant:
|
|
assert self.quant_config.activation_scheme == "dynamic"
|
|
if current_platform.is_fp8_fnuz():
|
|
w13_weight, w13_weight_scale_inv, w13_input_scale = \
|
|
normalize_e4m3fn_to_e4m3fnuz(
|
|
layer.w13_weight, layer.w13_weight_scale_inv,
|
|
layer.w13_input_scale)
|
|
w2_weight, w2_weight_scale_inv, w2_input_scale = \
|
|
normalize_e4m3fn_to_e4m3fnuz(
|
|
layer.w2_weight, layer.w2_weight_scale_inv,
|
|
layer.w2_input_scale)
|
|
elif self.flashinfer_moe_backend is not None:
|
|
# NOTE: weights have to be swapped since the activation is
|
|
# applied on different half for flashinfer vs vllm
|
|
w13_weight = swap_w13_to_w31(layer.w13_weight.data)
|
|
w13_weight_scale_inv = swap_w13_to_w31(
|
|
layer.w13_weight_scale_inv.data)
|
|
w2_weight = layer.w2_weight.data
|
|
w2_weight_scale_inv = layer.w2_weight_scale_inv.data
|
|
else:
|
|
w13_weight = layer.w13_weight.data
|
|
w13_weight_scale_inv = layer.w13_weight_scale_inv.data
|
|
w2_weight = layer.w2_weight
|
|
w2_weight_scale_inv = layer.w2_weight_scale_inv
|
|
|
|
# torch.compile() cannot use Parameter subclasses.
|
|
layer.w13_weight = Parameter(w13_weight, requires_grad=False)
|
|
layer.w13_weight_scale_inv = Parameter(w13_weight_scale_inv,
|
|
requires_grad=False)
|
|
layer.w2_weight = Parameter(w2_weight, requires_grad=False)
|
|
layer.w2_weight_scale_inv = Parameter(w2_weight_scale_inv,
|
|
requires_grad=False)
|
|
if self.rocm_aiter_moe_enabled:
|
|
# reshaping weights is required for aiter moe kernel.
|
|
shuffled_w13, shuffled_w2 = shuffle_weights(
|
|
layer.w13_weight.data, layer.w2_weight.data)
|
|
|
|
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
|
|
requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(shuffled_w2,
|
|
requires_grad=False)
|
|
|
|
# DeepGemm scales need to be transposed and aligned. We try to do
|
|
# it ahead of time for performance reasons.
|
|
if self.allow_deep_gemm and not is_blackwell_deep_gemm_e8m0_used():
|
|
# Lazy import to avoid CUDA initialization problems.
|
|
if _is_col_major(layer.w13_weight_scale_inv):
|
|
layer.w13_weight_scale_inv = \
|
|
get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous()
|
|
if _is_col_major(layer.w2_weight_scale_inv):
|
|
layer.w2_weight_scale_inv = \
|
|
get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous()
|
|
|
|
# If checkpoint is fp16, quantize in place.
|
|
elif not self.quant_config.is_checkpoint_fp8_serialized:
|
|
fp8_dtype = current_platform.fp8_dtype()
|
|
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.local_num_experts,
|
|
dtype=torch.float32,
|
|
device=w13_weight.device),
|
|
requires_grad=False)
|
|
for expert in range(layer.local_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)
|
|
if self.rocm_aiter_moe_enabled:
|
|
# reshaping weights is required for aiter moe kernel.
|
|
shuffled_w13, shuffled_w2 = shuffle_weights(
|
|
layer.w13_weight, layer.w2_weight)
|
|
|
|
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
|
|
requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(shuffled_w2,
|
|
requires_grad=False)
|
|
# 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)):
|
|
logger.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 current_platform.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.local_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
|
|
|
|
if self.rocm_aiter_moe_enabled:
|
|
shuffled_w13, shuffled_w2 = shuffle_weights(
|
|
layer.w13_weight, layer.w2_weight)
|
|
|
|
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
|
|
requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(shuffled_w2,
|
|
requires_grad=False)
|
|
|
|
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
|
|
requires_grad=False)
|
|
|
|
if self.flashinfer_moe_backend is not None:
|
|
# NOTE: weights have to be swapped since the activation is
|
|
# applied on different half for flashinfer vs vllm
|
|
assert not self.block_quant
|
|
register_moe_scaling_factors(layer)
|
|
w13_weight = swap_w13_to_w31(layer.w13_weight.data)
|
|
if self.flashinfer_moe_backend == \
|
|
FlashinferMoeBackend.TENSORRT_LLM:
|
|
rotate_flashinfer_fp8_moe_weights(w13_weight, w2_weight)
|
|
layer.w13_weight.data = w13_weight.data
|
|
|
|
if self.use_marlin:
|
|
prepare_moe_fp8_layer_for_marlin(layer, False)
|
|
# Activations not quantized for marlin.
|
|
del layer.w13_input_scale
|
|
del layer.w2_input_scale
|
|
|
|
if is_blackwell_deep_gemm_e8m0_used():
|
|
assert layer.weight_block_size is not None
|
|
# Re-quantise the expert weights so their scales are UE8M0.
|
|
block_sz = tuple(layer.weight_block_size)
|
|
requant_weight_ue8m0_inplace(
|
|
layer.w13_weight.data,
|
|
layer.w13_weight_scale_inv.data,
|
|
block_sz,
|
|
)
|
|
requant_weight_ue8m0_inplace(
|
|
layer.w2_weight.data,
|
|
layer.w2_weight_scale_inv.data,
|
|
block_sz,
|
|
)
|
|
|
|
# Ensure column-major TMA alignment expected by DeepGEMM.
|
|
if _is_col_major(layer.w13_weight_scale_inv):
|
|
layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(
|
|
layer.w13_weight_scale_inv).contiguous()
|
|
if _is_col_major(layer.w2_weight_scale_inv):
|
|
layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(
|
|
layer.w2_weight_scale_inv).contiguous()
|
|
|
|
def select_gemm_impl(
|
|
self,
|
|
prepare_finalize: FusedMoEPrepareAndFinalize,
|
|
moe: FusedMoEConfig,
|
|
) -> FusedMoEPermuteExpertsUnpermute:
|
|
from vllm.model_executor.layers.fused_moe import (
|
|
BatchedTritonOrDeepGemmExperts, TritonOrDeepGemmExperts)
|
|
|
|
assert not self.use_marlin and not self.rocm_aiter_moe_enabled, (
|
|
"Marlin and ROCm AITER are not supported with all2all yet.")
|
|
|
|
if (prepare_finalize.activation_format ==
|
|
FusedMoEActivationFormat.BatchedExperts):
|
|
max_num_tokens_per_rank = (
|
|
prepare_finalize.max_num_tokens_per_rank())
|
|
assert max_num_tokens_per_rank is not None
|
|
logger.debug(
|
|
"BatchedTritonOrDeepGemmExperts(%s): "
|
|
"max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
|
|
self.__class__.__name__, max_num_tokens_per_rank,
|
|
self.quant_config.weight_block_size, False)
|
|
return BatchedTritonOrDeepGemmExperts(
|
|
max_num_tokens=max_num_tokens_per_rank,
|
|
num_dispatchers=prepare_finalize.num_dispatchers(),
|
|
use_fp8_w8a8=True,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
per_act_token_quant=False,
|
|
allow_deep_gemm=self.allow_deep_gemm,
|
|
)
|
|
elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
|
|
experts = select_cutlass_fp8_gemm_impl(
|
|
moe,
|
|
self.layer,
|
|
)
|
|
logger.debug_once("Using %s", experts.__class__.__name__)
|
|
return experts
|
|
else:
|
|
logger.debug(
|
|
"TritonOrDeepGemmExperts(%s): block_size=%s, per_act_token=%s",
|
|
self.__class__.__name__, self.quant_config.weight_block_size,
|
|
False)
|
|
return TritonOrDeepGemmExperts(
|
|
use_fp8_w8a8=True,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
allow_deep_gemm=self.allow_deep_gemm,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
enable_eplb: bool = False,
|
|
expert_load_view: Optional[torch.Tensor] = None,
|
|
logical_to_physical_map: Optional[torch.Tensor] = None,
|
|
logical_replica_count: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
if enable_eplb:
|
|
assert expert_load_view is not None
|
|
assert logical_to_physical_map is not None
|
|
assert logical_replica_count is not None
|
|
assert isinstance(layer, FusedMoE)
|
|
|
|
if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
|
|
assert activation == 'silu', (
|
|
f"Expected 'silu' activation but got {activation}")
|
|
assert scoring_func == 'sigmoid', (
|
|
f"Expected 'sigmoid' scoring func but got {scoring_func}")
|
|
if self.block_quant:
|
|
assert (renormalize and use_grouped_topk
|
|
and custom_routing_function is None)
|
|
|
|
return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
|
|
routing_logits=router_logits.to(torch.float32),
|
|
routing_bias=e_score_correction_bias,
|
|
x=x,
|
|
w13_weight=layer.w13_weight,
|
|
w13_weight_scale_inv=layer.w13_weight_scale_inv,
|
|
w2_weight=layer.w2_weight,
|
|
w2_weight_scale_inv=layer.w2_weight_scale_inv,
|
|
global_num_experts=global_num_experts,
|
|
top_k=top_k,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
intermediate_size=layer.intermediate_size_per_partition,
|
|
expert_offset=layer.ep_rank * layer.local_num_experts,
|
|
local_num_experts=layer.local_num_experts,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
routed_scaling=1.0,
|
|
)
|
|
else:
|
|
assert (not renormalize
|
|
and custom_routing_function is not None)
|
|
return apply_flashinfer_per_tensor_scale_fp8(
|
|
layer=layer,
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
routing_bias=e_score_correction_bias,
|
|
global_num_experts=global_num_experts,
|
|
top_k=top_k,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
apply_router_weight_on_input=apply_router_weight_on_input)
|
|
|
|
topk_weights, topk_ids = FusedMoE.select_experts(
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
use_grouped_topk=use_grouped_topk,
|
|
top_k=top_k,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
indices_type=self.topk_indices_dtype,
|
|
enable_eplb=enable_eplb,
|
|
expert_map=expert_map,
|
|
expert_load_view=expert_load_view,
|
|
logical_to_physical_map=logical_to_physical_map,
|
|
logical_replica_count=logical_replica_count,
|
|
)
|
|
|
|
if self.rocm_aiter_moe_enabled:
|
|
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( # noqa: E501
|
|
rocm_aiter_fused_experts)
|
|
return rocm_aiter_fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=activation,
|
|
use_fp8_w8a8=True,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
w1_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),
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
expert_map=expert_map)
|
|
elif self.use_marlin:
|
|
assert activation == "silu", (
|
|
f"{activation} not supported for Marlin MoE.")
|
|
return torch.ops.vllm.fused_marlin_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
None,
|
|
None,
|
|
layer.w13_weight_scale,
|
|
layer.w2_weight_scale,
|
|
router_logits,
|
|
topk_weights,
|
|
topk_ids,
|
|
quant_type_id=scalar_types.float8_e4m3fn.id,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map)
|
|
elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
|
|
assert self.block_quant is None
|
|
assert (not renormalize and custom_routing_function is not None)
|
|
assert activation == 'silu', (
|
|
f"Expected 'silu' activation but got {activation}")
|
|
assert scoring_func == 'sigmoid', (
|
|
f"Expected 'sigmoid' scoring func but got {scoring_func}")
|
|
if self.fused_experts is not None:
|
|
return self.fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
inplace=False,
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
else:
|
|
return flashinfer_cutlass_moe_fp8(
|
|
x,
|
|
layer,
|
|
topk_weights,
|
|
topk_ids,
|
|
inplace=False,
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
else:
|
|
from vllm.model_executor.layers.fused_moe import fused_experts
|
|
return fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=True,
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
expert_map=expert_map,
|
|
w1_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),
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
use_fp8_w8a8=True,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
allow_deep_gemm=self.allow_deep_gemm,
|
|
allow_cutlass_block_scaled_grouped_gemm=(
|
|
self.allow_cutlass_block_scaled_grouped_gemm))
|
|
|
|
|
|
class Fp8KVCacheMethod(BaseKVCacheMethod):
|
|
"""
|
|
Supports loading kv-cache scaling factors from FP8 checkpoints.
|
|
"""
|
|
|
|
def __init__(self, quant_config: Fp8Config):
|
|
super().__init__(quant_config)
|