from typing import Any, Dict, List, Optional import torch from torch.nn.parameter import Parameter from aphrodite import _custom_ops as ops from aphrodite.modeling.layers.linear import LinearBase, LinearMethodBase from aphrodite.modeling.parameter import (BaseAphroditeParameter, ChannelQuantScaleParameter, GroupQuantScaleParameter, PackedAphroditeParameter) from aphrodite.quantization.base_config import QuantizationConfig MARLIN_QQQ_TILE = 16 MARLIN_QQQ_MIN_THREAD_N = 64 MARLIN_QQQ_MIN_THREAD_K = 128 MARLIN_QQQ_MAX_PARALLEL = 16 MARLIN_QQQ_SUPPORTED_NUM_BITS = [4] MARLIN_QQQ_SUPPORTED_GROUP_SIZES = [-1, 128] MARLIN_QQQ_SUPPORTED_SYM = [True] class QQQConfig(QuantizationConfig): """Config class for QQQ Reference: https://arxiv.org/pdf/2406.09904 """ def __init__( self, weight_bits: int, group_size: int, is_sym: bool = True, ) -> None: self.weight_bits = weight_bits self.group_size = group_size self.is_sym = is_sym # Verify if self.weight_bits not in MARLIN_QQQ_SUPPORTED_NUM_BITS: raise ValueError( f"QQQ does not support weight_bits = {self.weight_bits}. " f"Only weight_bits = {MARLIN_QQQ_SUPPORTED_NUM_BITS} " "are supported.") if self.group_size not in MARLIN_QQQ_SUPPORTED_GROUP_SIZES: raise ValueError( f"QQQ does not support group_size = {self.group_size}. " f"Only group_sizes = {MARLIN_QQQ_SUPPORTED_GROUP_SIZES} " "are supported.") if self.is_sym not in MARLIN_QQQ_SUPPORTED_SYM: raise ValueError( f"QQQ does not support is_sym = {self.is_sym}. " f"Only sym = {MARLIN_QQQ_SUPPORTED_SYM} are supported.") # 4 Bits packed into 32 bit datatype. self.pack_factor = 32 // self.weight_bits # Tile size used by QQQ kernels. self.tile_size = MARLIN_QQQ_TILE # Min out_features dim self.min_n_threads = MARLIN_QQQ_MIN_THREAD_N # Min in_features dim self.min_k_threads = MARLIN_QQQ_MIN_THREAD_K # Max parallel problems to solve at once (improves large # batch performance) self.max_parallel = MARLIN_QQQ_MAX_PARALLEL # Permutation length used by the QQQ kernels. self.perm_len = 1024 def __repr__(self) -> str: return "QQQConfig(weight_bits={}, group_size={})".format( self.weight_bits, self.group_size) @classmethod def get_name(cls) -> str: return "qqq" @classmethod def get_supported_act_dtypes(cls) -> List[torch.dtype]: return [torch.half] @classmethod def get_min_capability(cls) -> int: return 80 @classmethod def get_config_filenames(cls) -> List[str]: """List of filenames to search for in the model directory.""" return [ "quant_config.json", "quantize_config.json", ] @classmethod def from_config(cls, config: Dict[str, Any]) -> "QQQConfig": weight_bits = cls.get_from_keys(config, ["wbits"]) group_size = cls.get_from_keys(config, ["group_size"]) return cls(weight_bits, group_size) def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["QQQLinearMethod"]: if isinstance(layer, LinearBase): return QQQLinearMethod(self) return None def get_scaled_act_names(self) -> List[str]: return [] class QQQLinearMethod(LinearMethodBase): """Linear method for QQQ. Args: quant_config: The QQQ quantization config. """ def __init__(self, quant_config: QQQConfig): self.quant_config = quant_config def create_weights( self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: List[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): weight_loader = extra_weight_attrs["weight_loader"] if params_dtype != torch.float16: raise ValueError( f"The params dtype must be float16, but got {params_dtype}") # Validate output_size_per_partition output_size_per_partition = sum(output_partition_sizes) if output_size_per_partition % self.quant_config.min_n_threads != 0: raise ValueError( f"Weight output_size_per_partition = " f"{output_size_per_partition} is not divisible by " f"min_n_threads = {self.quant_config.min_n_threads}.") if output_size_per_partition % self.quant_config.pack_factor != 0: raise ValueError( f"Weight output_size_per_partition = " f"{output_size_per_partition} is not divisible by " f"pack_factor = {self.quant_config.pack_factor}.") # Validate input_size_per_partition if input_size_per_partition % self.quant_config.min_k_threads != 0: raise ValueError( f"Weight input_size_per_partition = " f"{input_size_per_partition} is not divisible by " f"min_k_threads = {self.quant_config.min_k_threads}.") if (self.quant_config.group_size != -1 and input_size_per_partition % self.quant_config.group_size != 0): raise ValueError(f"Weight input_size_per_partition = " f"{input_size_per_partition} is not divisible by " f"group_size = {self.quant_config.group_size}.") # Check that we have at least 4 tiles horizontally in the shard num_tiles_per_perm = self.quant_config.perm_len // ( self.quant_config.tile_size**2) if output_size_per_partition % num_tiles_per_perm != 0: raise ValueError( "Each permutation group must reside on the same gpu") # Quantized 4Bit weights packed into Int32. qweight = PackedAphroditeParameter( data=torch.empty( input_size_per_partition // self.quant_config.tile_size, output_size_per_partition * self.quant_config.tile_size // self.quant_config.pack_factor, device="cuda", dtype=torch.int32, ), input_dim=0, output_dim=1, packed_dim=1, packed_factor=self.quant_config.pack_factor, marlin_tile_size=self.quant_config.tile_size, weight_loader=weight_loader) s_channel = ChannelQuantScaleParameter(data=torch.empty( 1, output_size_per_partition, device="cuda", dtype=torch.float, ), weight_loader=weight_loader, output_dim=1) if self.quant_config.group_size == -1: s_group_data = torch.tensor( [], device="cuda", dtype=torch.half, ) else: s_group_data = torch.empty( input_size_per_partition // self.quant_config.group_size, output_size_per_partition, device="cuda", dtype=torch.half, ) s_group_attr = {"data": s_group_data, "weight_loader": weight_loader} if self.quant_config.group_size == -1: s_group = BaseAphroditeParameter(**s_group_attr) else: s_group = GroupQuantScaleParameter(output_dim=1, input_dim=0, **s_group_attr) # Allocate workspace (Used for internal locking mechanism) max_workspace_size = ( output_size_per_partition // self.quant_config.min_n_threads) * self.quant_config.max_parallel workspace = BaseAphroditeParameter(data=torch.zeros(max_workspace_size, device="cuda", dtype=torch.int), weight_loader=weight_loader) layer.register_parameter("B", qweight) layer.register_parameter("s_channel", s_channel) layer.register_parameter("s_group", s_group) layer.register_parameter("workspace", workspace) def process_weights_after_loading(self, layer: torch.nn.Module) -> None: # required by torch.compile layer.B = Parameter(layer.B.data, requires_grad=False) layer.s_channel = Parameter(layer.s_channel.data, requires_grad=False) layer.s_group = Parameter(layer.s_group.data, requires_grad=False) layer.workspace = Parameter(layer.workspace.data, requires_grad=False) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: qweight = layer.B s_ch = layer.s_channel s_group = layer.s_group workspace = layer.workspace x_2d = x.view(-1, x.shape[-1]) size_m = x_2d.shape[0] size_k = x_2d.shape[1] size_n = s_ch.shape[1] x_int8, s_tok, _ = ops.scaled_int8_quant(x_2d) output_2d = ops.marlin_qqq_gemm(x_int8, qweight, s_tok, s_ch, s_group, workspace, size_m, size_n, size_k) output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], )) if bias is not None: output.add_(bias) # In-place add return output