286 lines
9.4 KiB
Python
286 lines
9.4 KiB
Python
from typing import Any, Dict, List, Optional
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import torch
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from torch.nn.parameter import Parameter
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from vllm import _custom_ops as ops
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from vllm.logger import init_logger
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from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.utils import set_weight_attrs
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logger = init_logger(__name__)
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MARLIN_QQQ_TILE = 16
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MARLIN_QQQ_MIN_THREAD_N = 64
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MARLIN_QQQ_MIN_THREAD_K = 128
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MARLIN_QQQ_MAX_PARALLEL = 16
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MARLIN_QQQ_SUPPORTED_NUM_BITS = [4]
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MARLIN_QQQ_SUPPORTED_GROUP_SIZES = [-1, 128]
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MARLIN_QQQ_SUPPORTED_SYM = [True]
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class QQQConfig(QuantizationConfig):
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"""Config class for QQQ
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Reference: https://arxiv.org/pdf/2406.09904
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"""
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def __init__(
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self,
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weight_bits: int,
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group_size: int,
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is_sym: bool = True,
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) -> None:
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.is_sym = is_sym
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# Verify
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if self.weight_bits not in MARLIN_QQQ_SUPPORTED_NUM_BITS:
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raise ValueError(
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f"QQQ does not support weight_bits = {self.weight_bits}. "
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f"Only weight_bits = {MARLIN_QQQ_SUPPORTED_NUM_BITS} "
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"are supported.")
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if self.group_size not in MARLIN_QQQ_SUPPORTED_GROUP_SIZES:
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raise ValueError(
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f"QQQ does not support group_size = {self.group_size}. "
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f"Only group_sizes = {MARLIN_QQQ_SUPPORTED_GROUP_SIZES} "
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"are supported.")
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if self.is_sym not in MARLIN_QQQ_SUPPORTED_SYM:
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raise ValueError(
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f"QQQ does not support is_sym = {self.is_sym}. "
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f"Only sym = {MARLIN_QQQ_SUPPORTED_SYM} are supported.")
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# 4 Bits packed into 32 bit datatype.
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self.pack_factor = 32 // self.weight_bits
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# Tile size used by QQQ kernels.
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self.tile_size = MARLIN_QQQ_TILE
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# Min out_features dim
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self.min_n_threads = MARLIN_QQQ_MIN_THREAD_N
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# Min in_features dim
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self.min_k_threads = MARLIN_QQQ_MIN_THREAD_K
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# Max parallel problems to solve at once (improves large
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# batch performance)
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self.max_parallel = MARLIN_QQQ_MAX_PARALLEL
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# Permutation length used by the QQQ kernels.
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self.perm_len = 1024
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def __repr__(self) -> str:
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return "QQQConfig(weight_bits={}, group_size={})".format(
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self.weight_bits, self.group_size)
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@classmethod
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def get_name(cls) -> str:
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return "qqq"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [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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"""List of filenames to search for in the model directory."""
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return [
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"quant_config.json",
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"quantize_config.json",
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]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "QQQConfig":
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weight_bits = cls.get_from_keys(config, ["wbits"])
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group_size = cls.get_from_keys(config, ["group_size"])
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return cls(weight_bits, group_size)
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QQQLinearMethod"]:
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if isinstance(layer, LinearBase):
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return QQQLinearMethod(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 QQQLinearMethod(LinearMethodBase):
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"""Linear method for QQQ.
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Args:
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quant_config: The QQQ quantization config.
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"""
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def __init__(self, quant_config: QQQConfig):
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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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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if params_dtype != torch.float16:
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raise ValueError(
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f"The params dtype must be float16, but got {params_dtype}")
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# Validate output_size_per_partition
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output_size_per_partition = sum(output_partition_sizes)
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if output_size_per_partition % self.quant_config.min_n_threads != 0:
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raise ValueError(
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f"Weight output_size_per_partition = "
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f"{output_size_per_partition} is not divisible by "
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f"min_n_threads = {self.quant_config.min_n_threads}.")
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if output_size_per_partition % self.quant_config.pack_factor != 0:
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raise ValueError(
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f"Weight output_size_per_partition = "
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f"{output_size_per_partition} is not divisible by "
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f"pack_factor = {self.quant_config.pack_factor}.")
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# Validate input_size_per_partition
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if input_size_per_partition % self.quant_config.min_k_threads != 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"min_k_threads = {self.quant_config.min_k_threads}.")
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if (self.quant_config.group_size != -1 and
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input_size_per_partition % self.quant_config.group_size != 0):
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raise ValueError(f"Weight input_size_per_partition = "
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f"{input_size_per_partition} is not divisible by "
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f"group_size = {self.quant_config.group_size}.")
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# Check that we have at least 4 tiles horizontally in the shard
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num_tiles_per_perm = self.quant_config.perm_len // (
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self.quant_config.tile_size**2)
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if output_size_per_partition % num_tiles_per_perm != 0:
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raise ValueError(
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"Each permutation group must reside on the same gpu")
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# Quantized 4Bit weights packed into Int32.
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qweight = Parameter(
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torch.empty(
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input_size_per_partition // self.quant_config.tile_size,
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output_size_per_partition * self.quant_config.tile_size //
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self.quant_config.pack_factor,
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device="cuda",
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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set_weight_attrs(
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qweight,
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{
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"input_dim": 0,
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"output_dim": 1,
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"packed_dim": 1,
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"pack_factor": self.quant_config.pack_factor,
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"marlin_tile_size": self.quant_config.tile_size,
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},
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)
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s_channel = Parameter(
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torch.empty(
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1,
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output_size_per_partition,
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device="cuda",
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dtype=torch.float,
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),
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requires_grad=False,
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)
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set_weight_attrs(
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s_channel,
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{
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"input_dim": None,
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"output_dim": 1,
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},
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)
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if self.quant_config.group_size == -1:
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s_group = Parameter(
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torch.tensor(
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[],
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device="cuda",
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dtype=torch.half,
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),
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requires_grad=False,
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)
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else:
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s_group = Parameter(
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torch.empty(
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input_size_per_partition // self.quant_config.group_size,
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output_size_per_partition,
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device="cuda",
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dtype=torch.half,
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),
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requires_grad=False,
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)
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set_weight_attrs(
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s_group,
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{
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"input_dim": None if self.quant_config.group_size == -1 else 0,
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"output_dim":
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None if self.quant_config.group_size == -1 else 1,
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},
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)
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# Allocate workspace (Used for internal locking mechanism)
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max_workspace_size = (
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output_size_per_partition //
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self.quant_config.min_n_threads) * self.quant_config.max_parallel
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workspace = Parameter(torch.zeros(max_workspace_size,
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device="cuda",
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dtype=torch.int),
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requires_grad=False)
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layer.register_parameter("B", qweight)
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set_weight_attrs(qweight, extra_weight_attrs)
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layer.register_parameter("s_channel", s_channel)
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set_weight_attrs(s_channel, extra_weight_attrs)
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layer.register_parameter("s_group", s_group)
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set_weight_attrs(s_group, extra_weight_attrs)
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layer.register_parameter("workspace", workspace)
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set_weight_attrs(workspace, extra_weight_attrs)
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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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qweight = layer.B
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s_ch = layer.s_channel
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s_group = layer.s_group
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workspace = layer.workspace
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x_2d = x.view(-1, x.shape[-1])
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size_m = x_2d.shape[0]
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size_k = x_2d.shape[1]
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size_n = s_ch.shape[1]
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x_int8, s_tok = ops.scaled_int8_quant(x_2d)
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output_2d = ops.marlin_qqq_gemm(x_int8, qweight, s_tok, s_ch, s_group,
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workspace, size_m, size_n, size_k)
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output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
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if bias is not None:
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output.add_(bias) # In-place add
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return output
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