bitsandbytes.py

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__ < "0.44.0":
                raise ImportError("bitsandbytes version is wrong. Please "
                                  "install bitsandbytes>=0.44.0.")
        except ImportError as err:
            raise ImportError("Please install bitsandbytes>=0.44.0 via "
                              "`pip install bitsandbytes>=0.44.0` 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