parameter.py

from typing import Callable, Optional, Union

import torch
from torch.nn import Parameter

from vllm.distributed import get_tensor_model_parallel_rank
from vllm.logger import init_logger

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

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, **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
        """

        self._weight_loader = weight_loader

    @property
    def weight_loader(self):
        return self._weight_loader

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

    def load_column_parallel_weight(self, loaded_weight: torch.Tensor):
        self._assert_and_load(loaded_weight)

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor):
        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)


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):
        tp_rank = get_tensor_model_parallel_rank()
        shard_size = self.data.shape[self.output_dim]
        loaded_weight = loaded_weight.narrow(self.output_dim,
                                             tp_rank * shard_size, shard_size)
        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")
        if isinstance(
                self,
                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

        tp_rank = get_tensor_model_parallel_rank()
        param_data = param_data.narrow(self.output_dim, shard_offset,
                                       shard_size)
        loaded_weight = loaded_weight.narrow(self.output_dim,
                                             tp_rank * shard_size, shard_size)
        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")

        if isinstance(
                self,
                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
        tp_rank = get_tensor_model_parallel_rank()
        shard_id = tp_rank if shard_id == "q" else tp_rank // num_heads
        param_data = param_data.narrow(self.output_dim, shard_offset,
                                       shard_size)
        loaded_weight = loaded_weight.narrow(self.output_dim,
                                             shard_id * shard_size, shard_size)

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


class ModelWeightParameter(_ColumnvLLMParameter):
    """
    Parameter class for linear layer weights. Extends the
    _ColumnvLLMParameter by adding loading functionality
    for linear layers with row parallel functionality.
    Requires an input dimension 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):
        tp_rank = get_tensor_model_parallel_rank()
        shard_size = self.data.shape[self.input_dim]
        loaded_weight = loaded_weight.narrow(self.input_dim,
                                             tp_rank * shard_size, shard_size)

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

        assert self.data.shape == loaded_weight.shape
        self.data.copy_(loaded_weight)


class GroupQuantScaleParameter(ModelWeightParameter):
    """
    Parameter class for weight scales loaded for weights with
    grouped quantization. Equivalent to ModelWeightParameter.
    """
    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):
        self.qkv_idxs = {"q": 0, "k": 1, "v": 2}
        super().__init__(**kwargs)

    def _shard_id_as_int(self, shard_id: Union[str, int]) -> int:
        if isinstance(shard_id, int):
            return shard_id

        assert isinstance(shard_id, str)
        assert shard_id in self.qkv_idxs
        return self.qkv_idxs[shard_id]

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

    def _load_into_shard_id(self, loaded_weight: torch.Tensor,
                            shard_id: Union[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 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,
                 packed_dim: int,
                 marlin_tile_size: Optional[int] = None,
                 **kwargs):
        self._packed_factor = packed_factor
        self._packed_dim = packed_dim
        self._marlin_tile = marlin_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(self):
        return self._marlin_tile

    def _adjust_shard_indexes_for_marlin(self, shard_size, shard_offset):
        return shard_size * self.marlin_tile, shard_offset * self.marlin_tile

    def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
        shard_size = shard_size // self.packed_factor
        shard_offset = shard_offset // self.packed_factor
        if self.marlin_tile is not None:
            return self._adjust_shard_indexes_for_marlin(
                shard_size, shard_offset)
        return shard_size, shard_offset