parameter.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from collections.abc import Callable, Hashable
from fractions import Fraction
from weakref import WeakValueDictionary

import torch
from torch.nn import Parameter
import vllm.envs as envs


from vllm.distributed import (
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from vllm.logger import init_logger

__all__ = [
    "BasevLLMParameter",
    "PackedvLLMParameter",
    "PerTensorScaleParameter",
    "ModelWeightParameter",
    "ChannelQuantScaleParameter",
    "GroupQuantScaleParameter",
    "PackedColumnParameter",
    "RowvLLMParameter",
]

logger = init_logger(__name__)


class BasevLLMParameter(Parameter):
    """
    Base parameter for vLLM linear layers. Extends the torch.nn.parameter
    by taking in a linear weight loader. Will copy the loaded weight
    into the parameter when the provided weight loader is called.
    """

    def __new__(cls, data: torch.Tensor | None, **kwargs):
        return super().__new__(cls, data=data, requires_grad=False)

    def __init__(self, data: torch.Tensor, weight_loader: Callable):
        """
        Initialize the BasevLLMParameter

        :param data: torch tensor with the parameter data
        :param weight_loader: weight loader callable

        :returns: a torch.nn.parameter
        """

        # During weight loading, we often do something like:
        # narrowed_tensor = param.data.narrow(0, offset, len)
        # narrowed_tensor.copy_(real_weight)
        # expecting narrowed_tensor and param.data to share the same storage.
        # However, on TPUs, narrowed_tensor will lazily propagate to the base
        # tensor, which is param.data, leading to the redundant memory usage.
        # This sometimes causes OOM errors during model loading. To avoid this,
        # we sync the param tensor after its weight loader is called.
        from vllm.platforms import current_platform

        if current_platform.use_sync_weight_loader():
            weight_loader = current_platform.make_synced_weight_loader(weight_loader)

        self._weight_loader = weight_loader
        self.tp_rank = get_tensor_model_parallel_rank()
        self.tp_size = get_tensor_model_parallel_world_size()

    @property
    def weight_loader(self) -> Callable:
        # NOTE(@ksayers) some models such as mamba_mixer2 override the
        # weight loader to support custom loading. In the future, model-specific
        # weight loading should be implemented via Model.load_weights. In the
        # meantime, support deleting and overriding `weight_loader` attribute
        if self._weight_loader is None:
            raise AttributeError(
                f"{self.__class__.__name__} weight_loader attribute has been deleted"
            )
        return self._weight_loader

    @weight_loader.setter
    def weight_loader(self, value: Callable):
        self._weight_loader = value

    @weight_loader.deleter
    def weight_loader(self):
        self._weight_loader = None  # type: ignore[assignment]

    def _is_1d_and_scalar(self, loaded_weight: torch.Tensor):
        cond1 = self.data.ndim == 1 and self.data.numel() == 1
        cond2 = loaded_weight.ndim == 0 and loaded_weight.numel() == 1
        return cond1 and cond2

    def _assert_and_load(self, loaded_weight: torch.Tensor):
        assert self.data.shape == loaded_weight.shape or self._is_1d_and_scalar(
            loaded_weight
        )
        self.data.copy_(loaded_weight)

    def load_column_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
        self._assert_and_load(loaded_weight)

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
        self._assert_and_load(loaded_weight)

    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
        self._assert_and_load(loaded_weight)

    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
        self._assert_and_load(loaded_weight)

    def _shard_id_as_int(self, shard_id: str | int) -> int:
        if isinstance(shard_id, int):
            return shard_id

        # if not int, assume shard_id for qkv
        # map to int and return
        qkv_idxs = {"q": 0, "k": 1, "v": 2}
        assert isinstance(shard_id, str)
        assert shard_id in qkv_idxs
        return qkv_idxs[shard_id]

    @classmethod
    def __torch_function__(cls, func, types, args=(), kwargs=None):
        if kwargs is None:
            kwargs = {}
        return super().__torch_function__(func, types, args, kwargs)


class _ColumnvLLMParameter(BasevLLMParameter):
    """
    Private class defining weight loading functionality
    (load_merged_column_weight, load_qkv_weight)
    for parameters being loaded into linear layers with column
    parallelism. This includes QKV and MLP layers which are
    not already fused on disk. Requires an output dimension
    to be defined. Called within the weight loader of
    each of the column parallel linear layers.
    """

    def __init__(self, output_dim: int, **kwargs):
        self._output_dim = output_dim
        super().__init__(**kwargs)

    @property
    def output_dim(self):
        return self._output_dim

    def load_column_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
        if not envs.VLLM_USE_NN or len( self.data.shape)==1 or is_quantization:
            shard_size = self.data.shape[self.output_dim]
        else:
            shard_size = self.data.shape[int(not(self.output_dim))]
        loaded_weight = loaded_weight.narrow(
            self.output_dim, self.tp_rank * shard_size, shard_size
        )
        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert self.data.shape == loaded_weight.shape
        self.data.copy_(loaded_weight)

    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
        shard_offset = kwargs.get("shard_offset")
        shard_size = kwargs.get("shard_size")
        is_quantization = kwargs.get("is_quantization")

        # TODO: move these to PackedColumnParameter and PackedvLLMParameter
        if (
            isinstance(self, (PackedColumnParameter, PackedvLLMParameter))
            and self.packed_dim == self.output_dim
        ):
            shard_size, shard_offset = self.adjust_shard_indexes_for_packing(
                shard_offset=shard_offset, shard_size=shard_size
            )

        param_data = self.data

        if not envs.VLLM_USE_NN or is_quantization:
            param_data = param_data.narrow(self.output_dim, shard_offset, shard_size)
        else:
            param_data = param_data.narrow(int(not(self.output_dim)), shard_offset, shard_size)
        loaded_weight = loaded_weight.narrow(
            self.output_dim, self.tp_rank * shard_size, shard_size
        )
        
        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()

        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
        shard_offset = kwargs.get("shard_offset")
        shard_size = kwargs.get("shard_size")
        shard_id = kwargs.get("shard_id")
        num_heads = kwargs.get("num_heads")
        is_quantization = kwargs.get("is_quantization")

        # TODO: move these to PackedColumnParameter and PackedvLLMParameter
        if (
            isinstance(self, (PackedColumnParameter, PackedvLLMParameter))
            and self.output_dim == self.packed_dim
        ):
            shard_size, shard_offset = self.adjust_shard_indexes_for_packing(
                shard_offset=shard_offset, shard_size=shard_size
            )

        param_data = self.data
        shard_id = self.tp_rank if shard_id == "q" else self.tp_rank // num_heads
        if not envs.VLLM_USE_NN or len(param_data.shape)==1 or is_quantization:
            param_data = param_data.narrow(self.output_dim, shard_offset, shard_size)
        else:
            param_data = param_data.narrow(int(not(self.output_dim)), shard_offset, shard_size)
        loaded_weight = loaded_weight.narrow(
            self.output_dim, shard_id * shard_size, shard_size
        )

        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)


class RowvLLMParameter(BasevLLMParameter):
    """
    Parameter class defining weight_loading functionality
    (load_row_parallel_weight) for parameters being loaded
    into linear layers with row parallel functionality.
    Requires an input_dim to be defined.
    """

    def __init__(self, input_dim: int, **kwargs):
        self._input_dim = input_dim
        super().__init__(**kwargs)

    @property
    def input_dim(self):
        return self._input_dim

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
        if not envs.VLLM_USE_NN or is_quantization:
            shard_size = self.data.shape[self.input_dim]
        else:
            shard_size = self.data.shape[int(not(self.input_dim))]
        loaded_weight = loaded_weight.narrow(
            self.input_dim, self.tp_rank * shard_size, shard_size
        )

        if len(loaded_weight.shape) == 0:
            loaded_weight = loaded_weight.reshape(1)

        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert self.data.shape == loaded_weight.shape
        self.data.copy_(loaded_weight)


class ModelWeightParameter(_ColumnvLLMParameter, RowvLLMParameter):
    """
    Parameter class for linear layer weights. Uses both column and
    row parallelism.
    """

    pass


class GroupQuantScaleParameter(_ColumnvLLMParameter, RowvLLMParameter):
    """
    Parameter class for weight scales loaded for weights with
    grouped quantization. Uses both column and row parallelism.
    """

    pass


class ChannelQuantScaleParameter(_ColumnvLLMParameter):
    """
    Parameter class for weight scales loaded for weights with
    channel-wise quantization. Equivalent to _ColumnvLLMParameter.
    """

    pass


class PerTensorScaleParameter(BasevLLMParameter):
    """
    Parameter class for scales where the number of scales is
    equivalent to the number of logical matrices in fused linear
    layers (e.g. for QKV, there are 3 scales loaded from disk).
    This is relevant to weights with per-tensor quantization.
    Adds functionality to map the scalers to a shard during
    weight loading.

    Note: additional parameter manipulation may be handled
    for each quantization config specifically, within
    process_weights_after_loading
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    # For row parallel layers, no sharding needed
    # load weight into parameter as is
    def load_row_parallel_weight(self, *args, **kwargs):
        super().load_row_parallel_weight(*args, **kwargs)

    def load_merged_column_weight(self, *args, **kwargs):
        self._load_into_shard_id(*args, **kwargs)

    def load_qkv_weight(self, *args, **kwargs):
        self._load_into_shard_id(*args, **kwargs)

    def load_column_parallel_weight(self, *args, **kwargs):
        super().load_row_parallel_weight(*args, **kwargs)

    def _load_into_shard_id(
        self, loaded_weight: torch.Tensor, shard_id: str | int, **kwargs
    ):
        """
        Slice the parameter data based on the shard id for
        loading.
        """

        param_data = self.data
        shard_id = self._shard_id_as_int(shard_id)

        # AutoFP8 scales do not have a shape
        # compressed-tensors scales do have a shape
        if len(loaded_weight.shape) != 0:
            assert loaded_weight.shape[0] == 1
            loaded_weight = loaded_weight[0]

        param_data = param_data[shard_id]
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)


class PackedColumnParameter(_ColumnvLLMParameter):
    """
    Parameter for model parameters which are packed on disk
    and support column parallelism only. See PackedvLLMParameter
    for more details on the packed properties.
    """

    def __init__(
        self,
        packed_factor: int | Fraction,
        packed_dim: int,
        marlin_tile_size: int | None = None,
        bitblas_tile_size: int | None = None,
        **kwargs,
    ):
        self._packed_factor = packed_factor
        self._packed_dim = packed_dim
        self._marlin_tile_size = marlin_tile_size
        self._bitblas_tile_size = bitblas_tile_size
        super().__init__(**kwargs)

    @property
    def packed_dim(self):
        return self._packed_dim

    @property
    def packed_factor(self):
        return self._packed_factor

    @property
    def marlin_tile_size(self):
        return self._marlin_tile_size

    @property
    def bitblas_tile_size(self):
        return self._bitblas_tile_size

    def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
        return _adjust_shard_indexes_for_packing(
            shard_size=shard_size,
            shard_offset=shard_offset,
            packed_factor=self.packed_factor,
            marlin_tile_size=self.marlin_tile_size,
            bitblas_tile_size=self.bitblas_tile_size,
        )


class PackedvLLMParameter(ModelWeightParameter):
    """
    Parameter for model weights which are packed on disk.
    Example: GPTQ Marlin weights are int4 or int8, packed into int32.
    Extends the ModelWeightParameter to take in the
    packed factor, the packed dimension, and optionally, marlin
    tile size for marlin kernels. Adjusts the shard_size and
    shard_offset for fused linear layers model weight loading
    by accounting for packing and optionally, marlin tile size.
    """

    def __init__(
        self,
        packed_factor: int | Fraction,
        packed_dim: int,
        marlin_tile_size: int | None = None,
        bitblas_tile_size: int | None = None,
        **kwargs,
    ):
        self._packed_factor = packed_factor
        self._packed_dim = packed_dim
        self._marlin_tile_size = marlin_tile_size
        self._bitblas_tile_size = bitblas_tile_size
        super().__init__(**kwargs)

    @property
    def packed_dim(self):
        return self._packed_dim

    @property
    def packed_factor(self):
        return self._packed_factor

    @property
    def marlin_tile_size(self):
        return self._marlin_tile_size

    @property
    def bitblas_tile_size(self):
        return self._bitblas_tile_size

    def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
        return _adjust_shard_indexes_for_packing(
            shard_size=shard_size,
            shard_offset=shard_offset,
            packed_factor=self.packed_factor,
            marlin_tile_size=self.marlin_tile_size,
            bitblas_tile_size=self.bitblas_tile_size,
        )


class BlockQuantScaleParameter(_ColumnvLLMParameter, RowvLLMParameter):
    """
    Parameter class for weight scales loaded for weights with
    block-wise quantization. Uses both column and row parallelism.
    """

    pass


class SharedWeightParameter(BasevLLMParameter):
    """
    Parameter for weights with many shared tensors across a model

    For example, when applying transforms to the "gate" and "up" partitions of
    `MergedColumnParallelLinear`, the transform weights must stay separate
    tensors in order to allow for tensor memory sharing between layers.
    """

    # global registry for sharing tensors based on passed `data_key`
    # this dict holds weaksrefs to avoid memory leak after model cleanup
    tensors_registry: WeakValueDictionary = WeakValueDictionary()

    # local container for strong references to shared tensors
    # this set compensates for the fact that torch.nn.Parameter
    # and Parameter subclasses do not hold reliable references to tensors
    local_tensors: set[torch.Tensor]

    # dictionary mapping partition indices to associated parameters
    partitions: dict[int, ModelWeightParameter | Parameter]

    def __new__(cls, **kwargs):
        return super().__new__(cls, data=None, **kwargs)

    def __init__(self, input_dim: int = 1, output_dim: int = 0, **kwargs):
        weight_loader: Callable = kwargs.get("weight_loader")  # type: ignore[assignment]
        super().__init__(data=None, weight_loader=weight_loader)

        self.local_tensors = set()
        self.partitions = {}
        self.kwargs = {
            "input_dim": input_dim,
            "output_dim": output_dim,
            "weight_loader": self._fake_weight_loader,
        }

        if self.tp_size > 1:
            raise NotImplementedError(
                f"{self.__class__.__name__} does not "
                "currently support tensor parallelism"
            )

    def add_partition(self, index: int, data_key: Hashable, *args, **kwargs):
        """
        Add a partition to the weight parameter. Partitions whose `data_key`
        is the same will share tensor data

        :param index: index of partition to add
        :param data_key: hashable key used to key shared tensors
        :param *args: arguments for `torch.empty`
        :param **kwargs: keyword arguments for `torch.empty`
        """
        # load (shared) tensor using `data_key`
        if data_key not in self.tensors_registry:
            data = torch.empty(*args, **kwargs)
            self.tensors_registry[data_key] = data
        else:
            data = self.tensors_registry[data_key]

        # create associated model parameter
        self.partitions[index] = ModelWeightParameter(data=data, **self.kwargs)  # type: ignore[arg-type]

        # hold local reference, since ModelWeightParameter does not
        # see https://github.com/pytorch/pytorch/issues/75932
        self.local_tensors.add(data)

    def load_column_parallel_weight(self, loaded_weight: torch.Tensor):
        assert len(self.partitions) == 1 and 0 in self.partitions
        partition = self.partitions[0]

        ModelWeightParameter.load_column_parallel_weight(partition, loaded_weight)

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor):
        assert len(self.partitions) == 1 and 0 in self.partitions
        partition = self.partitions[0]

        ModelWeightParameter.load_row_parallel_weight(partition, loaded_weight)

    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
        partition_id = kwargs.pop("shard_id")
        partition_id = self._shard_id_as_int(partition_id)
        partition = self.partitions[partition_id]

        input_dim = self.kwargs.get("input_dim")
        shard_size = partition.data.size(input_dim) // self.tp_size
        shard_offset = self.tp_rank * shard_size

        ModelWeightParameter.load_merged_column_weight(
            partition, loaded_weight, shard_offset=shard_offset, shard_size=shard_size
        )

    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
        partition_id = self._shard_id_as_int(kwargs.pop("shard_id"))
        partition = self.partitions[partition_id]

        input_dim = self.kwargs.get("input_dim")
        shard_size = partition.data.size(input_dim) // self.tp_size
        shard_offset = self.tp_rank * shard_size
        shard_id = "q"  # fake first partition
        num_heads = kwargs.get("num_heads")

        ModelWeightParameter.load_qkv_weight(
            partition,
            loaded_weight,
            shard_offset=shard_offset,
            shard_size=shard_size,
            shard_id=shard_id,
            num_heads=num_heads,
        )

    def process_weights_after_loading(self):
        for key in self.partitions:
            self.partitions[key] = torch.nn.Parameter(
                data=self.partitions[key].data, requires_grad=False
            )

    @property
    def data(self):
        raise ValueError(
            "Accessing `data` of a "
            "`PartitionedModelWeightParameter` is not allowed. "
            "Instead, use `get_partition` to get the weight of "
            "the particular partition you want to access"
        )

    def _fake_weight_loader(
        self,
        param: BasevLLMParameter,
        loaded_weight: torch.Tensor,
        loaded_weight_shard_id: str | int | None,
    ):
        raise ValueError(
            "When loading partition weights of "
            f"{self.__class__.__name__}, use methods provided by "
            f"{self.__class__.__name__}, not partition loader"
        )


def permute_param_layout_(
    param: BasevLLMParameter, input_dim: int, output_dim: int, **kwargs
) -> BasevLLMParameter:
    """
    Permute a parameter's layout to the specified input and output dimensions,
    useful for forcing the parameter into a known layout, for example, if I need
    a packed (quantized) weight matrix to be in the layout
        {input_dim = 0, output_dim = 1, packed_dim = 0}
    then I can call:
        permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
    to ensure x is in the correct layout (permuting it to the correct layout if
    required, asserting if it cannot get it to the correct layout)
    """

    curr_input_dim = getattr(param, "input_dim", None)
    curr_output_dim = getattr(param, "output_dim", None)

    if curr_input_dim is None or curr_output_dim is None:
        assert param.data.dim() == 2, (
            "permute_param_layout_ only supports 2D parameters when either "
            "input_dim or output_dim is not set"
        )

    # if one of the dimensions is not set, set it to the opposite of the other
    #  we can only do this since we asserted the parameter is 2D above
    if curr_input_dim is None:
        assert curr_output_dim is not None, "either input or output dim must be set"
        curr_input_dim = (curr_output_dim + 1) % 2
    if curr_output_dim is None:
        assert curr_input_dim is not None, "either input or output dim must be set"
        curr_output_dim = (curr_input_dim + 1) % 2

    # create permutation from the current layout to the layout with
    # self.input_dim at input_dim and self.output_dim at output_dim preserving
    # other dimensions
    perm = [
        i for i in range(param.data.dim()) if i not in [curr_input_dim, curr_output_dim]
    ]
    perm.insert(input_dim, curr_input_dim)
    perm.insert(output_dim, curr_output_dim)

    if "packed_dim" in kwargs:
        assert (
            hasattr(param, "packed_dim")
            and param.packed_dim == perm[kwargs["packed_dim"]]
        ), "permute_param_layout_ currently doesn't support repacking"

    param.data = param.data.permute(*perm)
    if hasattr(param, "_input_dim"):
        param._input_dim = input_dim
    if hasattr(param, "_output_dim"):
        param._output_dim = output_dim
    if "packed_dim" in kwargs and hasattr(param, "_packed_dim"):
        param._packed_dim = kwargs["packed_dim"]

    return param


def _adjust_shard_indexes_for_marlin(shard_size, shard_offset, marlin_tile_size):
    return shard_size * marlin_tile_size, shard_offset * marlin_tile_size


def _adjust_shard_indexes_for_bitblas(shard_size, shard_offset, bitblas_tile_size):
    return shard_size // bitblas_tile_size, shard_offset // bitblas_tile_size


def _adjust_shard_indexes_for_packing(
    shard_size, shard_offset, packed_factor, marlin_tile_size, bitblas_tile_size
):
    shard_size = shard_size // packed_factor
    shard_offset = shard_offset // packed_factor
    if marlin_tile_size is not None:
        return _adjust_shard_indexes_for_marlin(
            shard_size=shard_size,
            shard_offset=shard_offset,
            marlin_tile_size=marlin_tile_size,
        )
    elif bitblas_tile_size is not None:
        return _adjust_shard_indexes_for_bitblas(
            shard_size=shard_size,
            shard_offset=shard_offset,
            bitblas_tile_size=bitblas_tile_size,
        )

    return shard_size, shard_offset