from typing import Any, Dict, List, Optional import torch from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, set_weight_attrs) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) class BitsAndBytesConfig(QuantizationConfig): """Config class for BitsAndBytes Quantization. Reference: https://arxiv.org/abs/2305.14314 """ def __init__( self, load_in_8bit: bool = False, load_in_4bit: bool = True, bnb_4bit_compute_dtype: str = "float32", bnb_4bit_quant_type: str = "fp4", bnb_4bit_use_double_quant: bool = False, llm_int8_enable_fp32_cpu_offload: bool = False, llm_int8_has_fp16_weight: bool = False, llm_int8_skip_modules: Optional[Any] = None, llm_int8_threshold: float = 0.0, ) -> None: self.load_in_8bit = load_in_8bit self.load_in_4bit = load_in_4bit self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype self.bnb_4bit_quant_type = bnb_4bit_quant_type self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload self.llm_int8_has_fp16_weight = llm_int8_has_fp16_weight self.llm_int8_skip_modules = llm_int8_skip_modules self.llm_int8_threshold = llm_int8_threshold def __repr__(self) -> str: return "BitsAndBytesConfig" @classmethod def get_name(self) -> str: return "bitsandbytes" @classmethod def get_supported_act_dtypes(self) -> List[torch.dtype]: return [torch.float32, torch.float16, torch.bfloat16] @classmethod def get_min_capability(cls) -> int: return 70 @staticmethod def get_config_filenames() -> List[str]: return [ "adapter_config.json", ] @classmethod def from_config(cls, config: Dict[str, Any]) -> "BitsAndBytesConfig": def get_safe_value(config, keys, default_value=None): try: value = cls.get_from_keys(config, keys) return value if value is not None else default_value except ValueError: return default_value load_in_8bit = get_safe_value(config, ["load_in_8bit"], default_value=False) load_in_4bit = get_safe_value(config, ["load_in_4bit"], default_value=True) bnb_4bit_compute_dtype = get_safe_value(config, ["bnb_4bit_compute_dtype"], default_value="float32") bnb_4bit_quant_type = get_safe_value(config, ["bnb_4bit_quant_type"], default_value="fp4") bnb_4bit_use_double_quant = get_safe_value( config, ["bnb_4bit_use_double_quant"], default_value=False) llm_int8_enable_fp32_cpu_offload = get_safe_value( config, ["llm_int8_enable_fp32_cpu_offload"], default_value=False) llm_int8_has_fp16_weight = get_safe_value(config, ["llm_int8_has_fp16_weight"], default_value=False) llm_int8_skip_modules = get_safe_value(config, ["llm_int8_skip_modules"], default_value=[]) llm_int8_threshold = get_safe_value(config, ["llm_int8_threshold"], default_value=0.0) return cls( load_in_8bit=load_in_8bit, load_in_4bit=load_in_4bit, bnb_4bit_compute_dtype=bnb_4bit_compute_dtype, bnb_4bit_quant_type=bnb_4bit_quant_type, bnb_4bit_use_double_quant=bnb_4bit_use_double_quant, llm_int8_enable_fp32_cpu_offload=llm_int8_enable_fp32_cpu_offload, llm_int8_has_fp16_weight=llm_int8_has_fp16_weight, llm_int8_skip_modules=llm_int8_skip_modules, llm_int8_threshold=llm_int8_threshold) def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["BitsAndBytesLinearMethod"]: if isinstance(layer, LinearBase): return BitsAndBytesLinearMethod(self) return None def get_scaled_act_names(self) -> List[str]: return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"] class BitsAndBytesLinearMethod(LinearMethodBase): """Linear method for BitsAndBytes. Args: quant_config: The BitsAndBytes quantization config. """ def __init__(self, quant_config: BitsAndBytesConfig): try: import bitsandbytes if bitsandbytes.__version__ < "0.42.0": raise ImportError("bitsandbytes version is wrong. Please " "install bitsandbytes>=0.42.0.") except ImportError as err: raise ImportError("Please install bitsandbytes>=0.42.0 via " "`pip install bitsandbytes>=0.42.0` to use " "bitsandbytes quantizer.") from err 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): from bitsandbytes.nn import Int8Params def calculate_quant_ratio(dtype): if dtype.is_floating_point: return torch.finfo(dtype).bits // torch.iinfo(torch.uint8).bits else: return torch.iinfo(dtype).bits // torch.iinfo(torch.uint8).bits def create_qweight_for_8bit(): qweight = Int8Params( data=torch.empty(sum(output_partition_sizes), input_size_per_partition, dtype=torch.int8), has_fp16_weights=self.quant_config.llm_int8_has_fp16_weight, requires_grad=False) set_weight_attrs( qweight, { "input_dim": 0, "output_dim": 0, "pack_factor": 1, "use_bitsandbytes_8bit": True, "generation": 0 }) return qweight def create_qweight_for_4bit(): quant_ratio = calculate_quant_ratio(params_dtype) total_size = input_size_per_partition * sum(output_partition_sizes) if total_size % quant_ratio != 0: raise ValueError( "The input size is not aligned with the quantized " "weight shape.") qweight = torch.nn.Parameter(torch.empty(total_size // quant_ratio, 1, dtype=torch.uint8), requires_grad=False) set_weight_attrs( qweight, { "input_dim": 0, "output_dim": 0, "pack_factor": quant_ratio, "use_bitsandbytes_4bit": True }) return qweight if self.quant_config.load_in_8bit: qweight = create_qweight_for_8bit() else: qweight = create_qweight_for_4bit() layer.register_parameter("qweight", qweight) set_weight_attrs(qweight, extra_weight_attrs) def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: if self.quant_config.load_in_8bit: return self._apply_8bit_weight(layer, x, bias) else: return self._apply_4bit_weight(layer, x, bias) def _apply_8bit_weight( self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: # only load the bitsandbytes module when needed from bitsandbytes import MatmulLtState, matmul original_type = x.dtype bf_x = x.to(torch.bfloat16) qweight = layer.qweight offsets = qweight.bnb_shard_offsets quant_states = qweight.bnb_quant_state matmul_states = qweight.matmul_state generation = qweight.generation out_dim_0 = x.shape[0] out_dim_1 = sum( [quant_state[1].shape[0] for quant_state in quant_states.items()]) out = torch.empty(out_dim_0, out_dim_1, dtype=torch.float16, device=x.device) current_index = 0 for i in range(len(quant_states)): output_size = quant_states[i].shape[0] # in profile_run or the first generation of inference, # create new matmul_states if generation == 0 or generation == 1: matmul_states[i] = MatmulLtState() matmul_states[i].CB = qweight[offsets[i]:offsets[i + 1]] matmul_states[i].SCB = quant_states[i] matmul_states[i].threshold = ( self.quant_config.llm_int8_threshold) matmul_states[i].has_fp16_weights = ( self.quant_config.llm_int8_has_fp16_weight) matmul_states[i].is_training = False if matmul_states[i].threshold > 0.0 and not matmul_states[ i].has_fp16_weights: matmul_states[i].use_pool = True new_x = bf_x.unsqueeze(0) out[:, current_index:current_index + output_size] = matmul( new_x, qweight[offsets[i]:offsets[i + 1]], state=matmul_states[i]) current_index += output_size # only update the matmul_states if it is not profile_run if (generation > 0 and not self.quant_config.llm_int8_has_fp16_weight and matmul_states[i].CB is not None and matmul_states[i].CxB is not None): del matmul_states[i].CB qweight[offsets[i]:offsets[i + 1]] = matmul_states[i].CxB out = out.to(original_type) if bias is not None: out += bias qweight.generation += 1 return out def _apply_4bit_weight( self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: # only load the bitsandbytes module when needed from bitsandbytes import matmul_4bit original_type = x.dtype bf_x = x.to(torch.bfloat16) qweight = layer.qweight quant_states = qweight.bnb_quant_state offsets = qweight.bnb_shard_offsets out_dim_0 = x.shape[0] out_dim_1 = sum( [quant_state[1].shape[0] for quant_state in quant_states.items()]) out = torch.empty(out_dim_0, out_dim_1, dtype=torch.bfloat16, device=x.device) current_index = 0 for i in range(len(quant_states)): output_size = quant_states[i].shape[0] # It is more efficient to use out kwarg like # matmul_4bit(..., out = ...). Infeasible now due to the bug # https://github.com/TimDettmers/bitsandbytes/issues/1235. # Need to change after the bug is fixed. out[:, current_index:current_index + output_size] = matmul_4bit( bf_x, qweight[offsets[i]:offsets[i + 1]].t(), quant_states[i]) current_index += output_size out = out.to(original_type) if bias is not None: out += bias return out