from typing import Any, Dict, List, Optional import torch from vllm import _custom_ops as ops from vllm.logger import init_logger from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.layers.quantization.utils.marlin_utils import ( apply_awq_marlin_linear, awq_to_marlin_zero_points, check_marlin_supported, marlin_make_empty_g_idx, marlin_make_workspace, marlin_permute_scales, replace_tensor, verify_marlin_supported, verify_marlin_supports_shape) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.parameter import (GroupQuantScaleParameter, PackedvLLMParameter) from vllm.scalar_type import scalar_types logger = init_logger(__name__) class AWQMarlinConfig(QuantizationConfig): """Config class for AWQ Marlin""" # num_bits -> type TYPE_MAP = { 4: scalar_types.uint4, 8: scalar_types.uint8, } def __init__(self, weight_bits: int, group_size: int, has_zp: bool, lm_head_quantized: bool) -> None: self.pack_factor = 32 // weight_bits # packed into int32 self.group_size = group_size self.has_zp = has_zp self.lm_head_quantized = lm_head_quantized if weight_bits not in self.TYPE_MAP: raise ValueError(f"Unsupported num_bits = {weight_bits}. " f"Supported num_bits = {self.TYPE_MAP.keys()}") self.quant_type = self.TYPE_MAP[weight_bits] verify_marlin_supported(self.quant_type, group_size=self.group_size, has_zp=self.has_zp) def __repr__(self) -> str: return (f"AWQMarlinConfig(quant_type={self.quant_type}, " f"group_size={self.group_size}, " f"has_zp={self.has_zp}, " f"lm_head_quantized={self.lm_head_quantized})") @classmethod def get_name(cls) -> str: return "awq_marlin" @classmethod def get_supported_act_dtypes(cls) -> List[torch.dtype]: return [torch.half, torch.bfloat16] @classmethod def get_min_capability(cls) -> int: return 80 @classmethod def get_config_filenames(cls) -> List[str]: return ["quantize_config.json"] @classmethod def from_config(cls, config: Dict[str, Any]) -> "AWQMarlinConfig": weight_bits = cls.get_from_keys(config, ["bits"]) group_size = cls.get_from_keys(config, ["group_size"]) has_zp = cls.get_from_keys(config, ["zero_point"]) lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False) return cls(weight_bits, group_size, has_zp, lm_head_quantized) @classmethod def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]: can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg) is_valid_user_quant = (user_quant is None or user_quant == "marlin" or user_quant == "awq_marlin") if can_convert and is_valid_user_quant: msg = ("The model is convertible to {} during runtime." " Using {} kernel.".format(cls.get_name(), cls.get_name())) logger.info(msg) return cls.get_name() if can_convert and user_quant == "awq": logger.info("Detected that the model can run with awq_marlin" ", however you specified quantization=awq explicitly," " so forcing awq. Use quantization=awq_marlin for" " faster inference") return None def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["AWQMarlinLinearMethod"]: if (isinstance(layer, LinearBase) or (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)): return AWQMarlinLinearMethod(self) return None def get_scaled_act_names(self) -> List[str]: return [] @classmethod def is_awq_marlin_compatible(cls, quant_config: Dict[str, Any]): # Extract data from quant config. quant_method = quant_config.get("quant_method", "").lower() num_bits = quant_config.get("bits", None) group_size = quant_config.get("group_size", None) has_zp = quant_config.get("zero_point", None) if quant_method != "awq": return False # If we cannot find the info needed in the config, cannot convert. if (num_bits is None or group_size is None or has_zp is None): return False if num_bits not in cls.TYPE_MAP: return False return check_marlin_supported(quant_type=cls.TYPE_MAP[num_bits], group_size=group_size, has_zp=has_zp) class AWQMarlinLinearMethod(LinearMethodBase): """Linear method for AWQ Marlin. Args: quant_config: The AWQ Marlin quantization config. """ def __init__(self, quant_config: AWQMarlinConfig) -> None: 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, ) -> None: del output_size output_size_per_partition = sum(output_partition_sizes) weight_loader = extra_weight_attrs.get("weight_loader") # Normalize group_size if self.quant_config.group_size != -1: group_size = self.quant_config.group_size else: group_size = input_size verify_marlin_supports_shape( output_size_per_partition=output_size_per_partition, input_size_per_partition=input_size_per_partition, input_size=input_size, group_size=group_size) qweight = PackedvLLMParameter( data=torch.empty( input_size_per_partition, output_size_per_partition // self.quant_config.pack_factor, dtype=torch.int32, ), input_dim=0, output_dim=1, packed_dim=1, packed_factor=self.quant_config.pack_factor, weight_loader=weight_loader) num_groups = input_size_per_partition // group_size qzeros = PackedvLLMParameter( data=torch.empty( num_groups, output_size_per_partition // self.quant_config.pack_factor, dtype=torch.int32, ), input_dim=0, output_dim=1, packed_dim=1, packed_factor=self.quant_config.pack_factor, weight_loader=weight_loader) scales = GroupQuantScaleParameter(data=torch.empty( num_groups, output_size_per_partition, dtype=params_dtype, ), input_dim=0, output_dim=1, weight_loader=weight_loader) layer.register_parameter("qweight", qweight) layer.register_parameter("qzeros", qzeros) layer.register_parameter("scales", scales) layer.input_size_per_partition = input_size_per_partition layer.output_size_per_partition = output_size_per_partition layer.num_groups = num_groups # TODO: Update this docs # Checkpoints are serialized in AutoAWQ format, which is different from the # marlin format. This function is called after the weights are loaded. # Here, we handle the repacking def process_weights_after_loading(self, layer: torch.nn.Module) -> None: device = layer.qweight.device layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False) layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False) layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False) # Allocate marlin workspace layer.workspace = marlin_make_workspace( layer.output_size_per_partition, device) # Repack weights from AWQ format to marlin format. marlin_qweight = ops.awq_marlin_repack( layer.qweight, size_k=layer.input_size_per_partition, size_n=layer.output_size_per_partition, num_bits=self.quant_config.quant_type.size_bits) replace_tensor(layer, "qweight", marlin_qweight) # Permute scales from AWQ format to marlin format. marlin_scales = marlin_permute_scales( layer.scales, size_k=layer.input_size_per_partition, size_n=layer.output_size_per_partition, group_size=self.quant_config.group_size) replace_tensor(layer, "scales", marlin_scales) # Permute zero-points from AWQ format to marlin format. marlin_zp = awq_to_marlin_zero_points( layer.qzeros, size_k=layer.num_groups, size_n=layer.output_size_per_partition, num_bits=self.quant_config.quant_type.size_bits) replace_tensor(layer, "qzeros", marlin_zp) # Not-used layer.g_idx = marlin_make_empty_g_idx(device) layer.g_idx_sort_indices = marlin_make_empty_g_idx(device) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: return apply_awq_marlin_linear( input=x, weight=layer.qweight, weight_scale=layer.scales, weight_zp=layer.qzeros, g_idx=layer.g_idx, g_idx_sort_indices=layer.g_idx_sort_indices, workspace=layer.workspace, quant_type=self.quant_config.quant_type, output_size_per_partition=layer.output_size_per_partition, input_size_per_partition=layer.input_size_per_partition, bias=bias)