from typing import Any, Dict, List, Optional import torch from torch.nn.parameter import Parameter 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.parameter import (BasevLLMParameter, ChannelQuantScaleParameter, GroupQuantScaleParameter, PackedvLLMParameter) from vllm.scalar_type import scalar_types logger = init_logger(__name__) GPTQ_MARLIN_24_TILE = 16 GPTQ_MARLIN_24_MIN_THREAD_N = 128 GPTQ_MARLIN_24_MIN_THREAD_K = 128 GPTQ_MARLIN_24_MAX_PARALLEL = 64 GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES = [ scalar_types.uint4b8, scalar_types.uint8b128 ] GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES = [-1, 128] class GPTQMarlin24Config(QuantizationConfig): """Config class for Marlin24. """ def __init__( self, weight_bits: int, group_size: int, ) -> None: quant_type = { 4: scalar_types.uint4b8, 8: scalar_types.uint8b128, }.get(weight_bits) self.group_size = group_size # Verify if quant_type is None or \ quant_type not in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES: raise ValueError( f"Marlin_24 does not support quant_type = {quant_type}. " f"Only weight_bits = {GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES} " "are supported.") if self.group_size not in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES: raise ValueError( f"Marlin_24 does not support group_size = {self.group_size}. " f"Only group_sizes = {GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES} " "are supported.") self.quant_type = quant_type # 4 Bits packed into 32 bit datatype. self.pack_factor = 32 // self.quant_type.size_bits # Tile size used by marlin kernels. self.tile_size = 16 # Min out_features dim self.min_n_threads = GPTQ_MARLIN_24_MIN_THREAD_N # Min in_features dim self.min_k_threads = GPTQ_MARLIN_24_MIN_THREAD_K # Max parallel problems to solve at once (improves large # batch performance) self.max_parallel = GPTQ_MARLIN_24_MAX_PARALLEL # Permutation length used by the marlin kernels. self.perm_len = 1024 def __repr__(self) -> str: return "Marlin24Config(quant_type={}, group_size={})".format( self.quant_type, self.group_size) @classmethod def get_name(cls) -> str: return "gptq_marlin_24" @classmethod def get_supported_act_dtypes(cls) -> List[torch.dtype]: return [torch.half] @classmethod # Need to figure it out 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]) -> "GPTQMarlin24Config": weight_bits = cls.get_from_keys(config, ["bits"]) group_size = cls.get_from_keys(config, ["group_size"]) return cls(weight_bits, group_size) @classmethod def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]: is_marlin_24_format = ( hf_quant_cfg.get("checkpoint_format") == "marlin_24") is_valid_user_quant = (user_quant is None or user_quant == "gptq" or user_quant == "gptq_marlin_24") if is_marlin_24_format and is_valid_user_quant: msg = ("The model is serialized in {} format. " "Using {} kernel.".format(cls.get_name(), cls.get_name())) logger.info(msg) return cls.get_name() return None def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["GPTQMarlin24LinearMethod"]: if isinstance(layer, LinearBase): return GPTQMarlin24LinearMethod(self) return None def get_scaled_act_names(self) -> List[str]: return [] class GPTQMarlin24LinearMethod(LinearMethodBase): """Linear method for Marlin24. Args: quant_config: The Marlin24 quantization config. """ def __init__(self, quant_config: GPTQMarlin24Config): 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, ): del output_size # Unused. 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 = PackedvLLMParameter( data=torch.empty( input_size_per_partition // self.quant_config.tile_size // 2, 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) # Meta meta = PackedvLLMParameter(data=torch.empty( input_size_per_partition // 8 // 2 // 2, output_size_per_partition * 2, device="cuda", dtype=torch.int16, ), input_dim=0, output_dim=1, packed_dim=1, packed_factor=1, marlin_tile_size=2, weight_loader=weight_loader) # Determine if channelwise or not input_groups = (1 if self.quant_config.group_size == -1 else input_size_per_partition // self.quant_config.group_size) weight_scale_args = { "data": torch.empty( input_groups, output_size_per_partition, device="cuda", dtype=params_dtype, ), "weight_loader": weight_loader } if input_groups == 1: scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args) else: scales = GroupQuantScaleParameter(output_dim=1, input_dim=0, **weight_scale_args) # 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 = BasevLLMParameter(data=torch.zeros(max_workspace_size, device="cuda", dtype=torch.int), weight_loader=weight_loader) layer.register_parameter("B_24", qweight) layer.register_parameter("B_meta", meta) layer.register_parameter("s", scales) layer.register_parameter("workspace", workspace) def process_weights_after_loading(self, layer: torch.nn.Module) -> None: # required by torch.compile layer.B_24 = Parameter(layer.B_24.data, requires_grad=False) layer.s = Parameter(layer.s.data, requires_grad=False) layer.B_meta = Parameter(layer.B_meta.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_24 meta = layer.B_meta scales = layer.s 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 = scales.shape[1] output_2d = ops.gptq_marlin_24_gemm(x_2d, qweight, meta, scales, workspace, self.quant_config.quant_type, 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