# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import Any, 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 import QuantizationMethods from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.parameter import (BasevLLMParameter, ChannelQuantScaleParameter, GroupQuantScaleParameter, PackedvLLMParameter) logger = init_logger(__name__) class MarlinConfig(QuantizationConfig): """Config class for Marlin. Reference: https://github.com/IST-DASLab/marlin/tree/master """ def __init__( self, group_size: int, lm_head_quantized: bool, ) -> None: super().__init__() # Group size for the quantization. self.group_size = group_size self.lm_head_quantized = lm_head_quantized if self.group_size != 128 and self.group_size != -1: raise ValueError( "Currently, only group size 128 and -1 (channelwise) " "is supported for Marlin, but got group_size of " f"{self.group_size}") # 4 Bits packed into 32 bit datatype. self.pack_factor = 32 // 4 # Tile size used by marlin kernels. self.tile_size = 16 # Min out_features dim self.min_n_threads = 64 # Min in_features dim self.min_k_threads = 128 # Max parallel problems to solve at once (improves large # batch performance) self.max_parallel = 16 # Permutation length used by the marlin kernels. self.perm_len = 1024 def __repr__(self) -> str: return (f"MarlinConfig(group_size={self.group_size}, " f"lm_head_quantized={self.lm_head_quantized})") @classmethod def get_name(cls) -> QuantizationMethods: return "marlin" @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]) -> "MarlinConfig": group_size = cls.get_from_keys(config, ["group_size"]) lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False) return cls(group_size, lm_head_quantized) @classmethod def override_quantization_method( cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]: # compat: autogptq >=0.8.0 use checkpoint_format: str # compat: autogptq <=0.7.1 is_marlin_format: bool is_marlin_format = (hf_quant_cfg.get("checkpoint_format") == "marlin" or hf_quant_cfg.get("is_marlin_format", False)) is_valid_user_quant = (user_quant is None or user_quant == "gptq" or user_quant == "marlin") if is_marlin_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["MarlinLinearMethod"]: if (isinstance(layer, LinearBase) or (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)): return MarlinLinearMethod(self) return None class MarlinLinearMethod(LinearMethodBase): """Linear method for Marlin. Args: quant_config: The Marlin quantization config. """ def __init__(self, quant_config: MarlinConfig): 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, 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) # 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", qweight) 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 = Parameter(layer.B.data, requires_grad=False) layer.s = Parameter(layer.s.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 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.marlin_gemm(x_2d, qweight, scales, 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