ColossalAI/colossalai/shardformer/layer/linear.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-

import math
from typing import Callable, List, Optional, Tuple, Union

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.distributed import ProcessGroup
from torch.nn.parameter import Parameter

from colossalai.lazy import LazyInitContext
from colossalai.nn import init as init
from colossalai.nn.layer.utils import divide
from colossalai.tensor.d_tensor.api import (
    is_distributed_tensor,
    shard_colwise,
    shard_rowwise,
    sharded_tensor_to_existing_param,
)

from ._operation import (
    gather_forward_split_backward,
    linear_gather_forward_reducescatter_backward,
    linear_reducescatter_forward_gather_backward,
    linear_with_async_comm,
    linear_with_grad_accum,
    reduce_forward,
    split_forward_gather_backward,
)
from .parallel_module import PaddingParallelModule, ParallelModule
from .utils import create_randomizer_with_offset, is_share_sp_tp

__all__ = ["LinearWithGradAccum", "Linear1D_Col", "Linear1D_Row"]


class LinearWithGradAccum(ParallelModule):
    r"""Linear layer with no parallelism.

    Args:
        in_features (int): size of each input sample.
        out_features (int): size of each output sample.
        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
        dtype (`torch.dtype`): The dtype of parameters, defaults to None.
        device (`torch.device`): The device of parameters, defaults to None.
        gather_output (bool, optional): If true, call all-gather on output and make Y available
                    to all GPUs, otherwise, every GPU will have its output
                    which is :math:`Y_i = XA_i`, defaults to False
        seq_parallel (`bool`): If set to ``True``, it will use sequence parallel, defaults to False.
        overlap (`bool`): If set to ``True``, it will overlap input all-gather with gradient computation during backward, defaults to False.
        skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
            which is preserved for kernel fusion, defaults to False
        weight_initializer (`typing.Callable`):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (`typing.Callable`):
            The initializer of bias, defaults to xavier uniform initializer.

    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = True,
        dtype: torch.dtype = None,
        device: torch.device = None,
        skip_bias_add: bool = False,
        weight: Optional[Parameter] = None,
        bias_: Optional[Parameter] = None,
        weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
        bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
        use_zbv: bool = False,
        **kwargs,
    ):
        super().__init__(weight=weight, bias_=bias_, **kwargs)

        # Keep input parameters
        self.in_features = in_features
        self.out_features = out_features
        self.skip_bias_add = skip_bias_add
        self.device = device
        self.use_zbv = use_zbv

        if skip_bias_add and not bias:
            raise ValueError("cannot skip bias addition if bias is None")

        # offset the seed with randomizer index and rank
        seed = torch.random.initial_seed()

        self.randomizer = create_randomizer_with_offset(seed, process_group=None)

        # sanity check
        if weight is not None:
            assert not bias or bias_ is not None, "bias_ must be provided if bias is True when weight is not None"
        else:
            assert bias_ is None, "bias_ must be None if weight is None"

        # Parameters.
        if weight is None:
            factory_kwargs = {"device": device, "dtype": dtype}
            self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs))
        else:
            weight.data = weight.data.to(device=device, dtype=dtype)
            self.weight = weight

        if bias:
            if bias_ is None:
                self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
            else:
                bias_.data = bias_.data.to(device=device, dtype=dtype)
                self.bias = bias_
        else:
            self.bias = None

        if weight is None:
            # init weights
            self.reset_parameters(weight_initializer, bias_initializer)

    @staticmethod
    def from_native_module(module: nn.Linear, **kwargs) -> ParallelModule:
        r"""
        Convert a native PyTorch linear layer to a parallelized linear layer.
        """
        LazyInitContext.materialize(module)
        # get the attributes
        in_features = module.in_features
        out_features = module.out_features
        bias = module.bias is not None
        device = module.weight.device

        linear_1d = LinearWithGradAccum(
            in_features=in_features,
            out_features=out_features,
            bias=bias,
            device=device,
            weight=module.weight,
            bias_=module.bias,
            **kwargs,
        )

        return linear_1d

    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        with self.randomizer.fork_rng(enable_cpu=True):
            fan_in, fan_out = self.in_features, self.out_features
            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
            if self.bias is not None:
                bias_initializer(self.bias, fan_in=fan_in)

    def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
        assert (
            input_.shape[-1] == self.weight.shape[-1]
        ), "Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.".format(
            input_.shape, self.weight.shape, self.weight.shape[-1]
        )

        # Set up backprop all-reduce.
        input_parallel = input_

        # Matrix multiply.
        bias = self.bias if not self.skip_bias_add else None
        output_parallel = linear_with_grad_accum(
            input_parallel,
            self.weight,
            bias,
            False,
            use_zbv=self.use_zbv,
        )

        output = output_parallel

        if self.skip_bias_add:
            return output, self.bias
        else:
            return output


class Linear1D_Col(ParallelModule):
    r"""Linear layer with column parallelism.

    The linear layer is defined as :math:`Y = XA + b`. A is parallelized along
    its second dimension as :math:`A = [A_1, ..., A_p]`.

    Args:
        in_features (int): size of each input sample.
        out_features (int): size of each output sample.
        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
        dtype (`torch.dtype`): The dtype of parameters, defaults to None.
        device (`torch.device`): The device of parameters, defaults to None.
        process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None.
        gather_output (bool, optional): If true, call all-gather on output and make Y available
                    to all GPUs, otherwise, every GPU will have its output
                    which is :math:`Y_i = XA_i`, defaults to False
        seq_parallel (`bool`): If set to ``True``, it will use sequence parallel, defaults to False.
        skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
            which is preserved for kernel fusion, defaults to False
        weight_initializer (`typing.Callable`):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (`typing.Callable`):
            The initializer of bias, defaults to xavier uniform initializer.

    More details about ``initializer`` please refer to
    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = True,
        dtype: torch.dtype = None,
        device: torch.device = None,
        process_group: ProcessGroup = None,
        gather_output: bool = False,
        seq_parallel_mode: str = None,
        seq_parallel_dim: int = 1,
        skip_bias_add: bool = False,
        weight: Optional[Parameter] = None,
        bias_: Optional[Parameter] = None,
        weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
        bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
        fp8_communication: bool = False,
        use_zbv: bool = False,
        **kwargs,
    ):
        super().__init__(weight=weight, bias_=bias_, **kwargs)

        # Keep input parameters
        self.in_features = in_features
        self.out_features = out_features
        self.gather_output = gather_output
        self.seq_parallel_mode = seq_parallel_mode
        self.seq_parallel_dim = seq_parallel_dim
        self.skip_bias_add = skip_bias_add
        self.device = device
        self.process_group = process_group
        self.fp8_communication = fp8_communication
        self.use_zbv = use_zbv

        if skip_bias_add and not bias:
            raise ValueError("cannot skip bias addition if bias is None")

        # offset the seed with randomizer index and rank
        seed = torch.random.initial_seed()
        self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)

        # sanity check
        if weight is not None:
            assert not bias or bias_ is not None, "bias_ must be provided if bias is True when weight is not None"
        else:
            assert bias_ is None, "bias_ must be None if weight is None"

        # Parameters.
        if weight is None:
            factory_kwargs = {"device": device, "dtype": dtype}
            self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs))
        else:
            weight.data = weight.data.to(device=device, dtype=dtype)
            self.weight = weight

        if not is_distributed_tensor(self.weight):
            sharded_weight = shard_rowwise(self.weight.data, self.process_group)
            sharded_tensor_to_existing_param(sharded_weight, self.weight)

        if bias:
            if bias_ is None:
                self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
            else:
                bias_.data = bias_.data.to(device=device, dtype=dtype)
                self.bias = bias_
            if not is_distributed_tensor(self.bias):
                sharded_bias = shard_colwise(self.bias.data, self.process_group)
                sharded_tensor_to_existing_param(sharded_bias, self.bias)
        else:
            self.bias = None

        if weight is None:
            # init weights
            self.reset_parameters(weight_initializer, bias_initializer)

    @staticmethod
    def from_native_module(
        module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], **kwargs
    ) -> ParallelModule:
        r"""
        Convert a native PyTorch linear layer to a parallelized linear layer.
        """
        LazyInitContext.materialize(module)
        # get the attributes
        in_features = module.in_features
        out_features = module.out_features
        bias = module.bias is not None
        device = module.weight.device
        # ensure only one process group is passed
        if isinstance(process_group, (list, tuple)):
            assert len(process_group) == 1, f"Expected only one process group, got {len(process_group)}."
            process_group = process_group[0]

        tp_size = dist.get_world_size(process_group)
        if out_features < tp_size:
            return module

        if out_features % tp_size != 0:
            raise ValueError(
                f"The size of out_features:{out_features} is not integer multiples of tensor parallel size: {tp_size}!"
            )

        linear_1d = Linear1D_Col(
            in_features=in_features,
            out_features=out_features,
            bias=bias,
            device=device,
            process_group=process_group,
            weight=module.weight,
            bias_=module.bias,
            **kwargs,
        )

        return linear_1d

    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        with self.randomizer.fork_rng(enable_cpu=True):
            fan_in, fan_out = self.in_features, self.out_features
            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
            if self.bias is not None:
                bias_initializer(self.bias, fan_in=fan_in)

    def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
        assert (
            input_.shape[-1] == self.weight.shape[-1]
        ), "Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.".format(
            input_.shape, self.weight.shape, self.weight.shape[-1]
        )

        # Set up backprop all-reduce.
        input_parallel = input_

        # Matrix multiply.
        bias = self.bias if not self.skip_bias_add else None

        if is_share_sp_tp(self.seq_parallel_mode):
            output_parallel = linear_gather_forward_reducescatter_backward(
                input_parallel,
                self.weight,
                bias,
                self.process_group,
                True,
                self.seq_parallel_dim,
                ring=self.seq_parallel_mode == "ring",
            )
        else:
            output_parallel = linear_with_async_comm(
                input_parallel,
                self.weight,
                bias,
                self.process_group,
                True,
                fp8_communication=self.fp8_communication,
                use_zbv=self.use_zbv,
            )
        if self.gather_output:
            # All-gather across the partitions.
            output = gather_forward_split_backward(
                output_parallel, dim=-1, process_group=self.process_group, fp8_communication=self.fp8_communication
            )
        else:
            output = output_parallel

        if self.skip_bias_add:
            return output, self.bias
        else:
            return output


class Linear1D_Row(ParallelModule):
    r"""Linear layer with row parallelism

    Args:
        in_features (int): size of each input sample.
        out_features (int): size of each output sample.
        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
        dtype (`torch.dtype`): The dtype of parameters, defaults to None.
        parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False.
        process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None.
        seq_parallel_mode (`str`): The type of sp mode, it will use sequence parallel when `seq_parallel_mode` is not None. Defaults to None.
        seq_parallel_dim (`int`): Which dim will sequence parallelism split and gather the sequence.
        skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
            which is preserved for kernel fusion, defaults to False
        weight_initializer (:class:`typing.Callable`, optional):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (:class:`typing.Callable`, optional):
            The initializer of bias, defaults to xavier uniform initializer.

    More details about ``initializer`` please refer to
    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = True,
        dtype: torch.dtype = None,
        device: torch.device = None,
        process_group: ProcessGroup = None,
        seq_parallel_mode: str = None,
        seq_parallel_dim: int = 1,
        parallel_input: bool = True,
        skip_bias_add: bool = False,
        weight: Optional[Parameter] = None,
        bias_: Optional[Parameter] = None,
        weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
        bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
        stream_chunk_num: int = 1,
        fp8_communication: bool = False,
        use_zbv: bool = False,
    ):
        super().__init__()

        self.stream_chunk_num = stream_chunk_num

        # Keep input parameters
        self.in_features = in_features
        self.out_features = out_features
        self.parallel_input = parallel_input
        self.skip_bias_add = skip_bias_add
        self.process_group = process_group
        self.seq_parallel_mode = seq_parallel_mode
        self.seq_parallel_dim = seq_parallel_dim
        self.num_partitions = dist.get_world_size(self.process_group)
        self.fp8_communication = fp8_communication
        self.use_zbv = use_zbv

        if skip_bias_add and not bias:
            raise ValueError("cannot skip bias addition if bias is None")

        # offset the seed with randomizer index and rank
        seed = torch.random.initial_seed()
        self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)

        # sanity check
        if weight is not None:
            assert not bias or bias_ is not None, "bias_ must be provided if bias is True when weight is not None"
        else:
            assert bias_ is None, "bias_ must be None if weight is None"

        # Parameters.
        if weight is None:
            # Initialize weight.
            factory_kwargs = {"device": device, "dtype": dtype}
            self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs))
        else:
            weight.data = weight.data.to(device=device, dtype=dtype)
            self.weight = weight
        if not is_distributed_tensor(self.weight):
            sharded_weight = shard_colwise(self.weight.data, self.process_group)
            sharded_tensor_to_existing_param(sharded_weight, self.weight)

        if self.stream_chunk_num > 1:
            # TODO() work for inference only
            self.chunk_weight()

        if bias:
            if bias_ is None:
                self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
            else:
                bias_.data = bias_.data.to(device=device, dtype=dtype)
                self.bias = bias_
        else:
            self.bias = None

        if weight is None:
            with self.randomizer.fork_rng(enable_cpu=True):
                self.reset_parameters(weight_initializer, bias_initializer)

    @staticmethod
    def from_native_module(
        module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], **kwargs
    ) -> ParallelModule:
        r"""
        Convert a native PyTorch linear layer to a parallelized linear layer.
        """
        LazyInitContext.materialize(module)
        # get the attributes
        in_features = module.in_features
        out_features = module.out_features
        bias = module.bias is not None
        device = module.weight.device

        # ensure only one process group is passed
        if isinstance(process_group, (list, tuple)):
            assert len(process_group) == 1, f"Expected only one process group, got {len(process_group)}."
            process_group = process_group[0]

        tp_size = dist.get_world_size(process_group)
        if in_features < tp_size:
            return module

        if in_features % tp_size != 0:
            raise ValueError(
                f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!"
            )

        linear_1d = Linear1D_Row(
            in_features=in_features,
            out_features=out_features,
            bias=bias,
            device=device,
            process_group=process_group,
            weight=module.weight,
            bias_=module.bias,
            **kwargs,
        )

        return linear_1d

    def chunk_weight(self):
        self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0)

    @torch.no_grad()
    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        fan_in, fan_out = self.in_features, self.out_features
        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)

        if self.bias is not None:
            bias_initializer(self.bias, fan_in=fan_in)
            if self.process_group is None:
                src_rank = 0
            else:
                src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0)

            origin_device = self.bias.device
            bias = self.bias.cuda()
            dist.broadcast(bias, src=src_rank, group=self.process_group)
            bias = bias.to(origin_device)
            self.bias.copy_(bias)

    def forward(self, input_: Tensor) -> Tensor:
        # Set up backprop all-reduce.
        if self.parallel_input:
            assert (
                input_.shape[-1] == self.weight.shape[-1]
            ), "Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.".format(
                input_.shape, self.weight.shape, self.weight.shape[-1]
            )
            input_ = input_
        else:
            assert (
                divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1]
            ), "Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.".format(
                input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions
            )
            input_ = split_forward_gather_backward(
                input_, dim=-1, process_group=self.process_group, fp8_communication=self.fp8_communication
            )

        if self.stream_chunk_num > 1:
            if self.training:
                raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!")
            with torch.no_grad():
                output_parallel_list = [None for i in range(self.stream_chunk_num)]
                handle_list = []
                for i in range(self.stream_chunk_num):
                    output_parallel_list[i] = F.linear(input_, self.weight_list[i])
                    handle = torch.distributed.all_reduce(
                        output_parallel_list[i], group=self.process_group, async_op=True
                    )
                    handle_list.append(handle)
                for handle in handle_list:
                    handle.wait()
                output = torch.cat(output_parallel_list, dim=-1)
        else:
            if is_share_sp_tp(self.seq_parallel_mode):
                output = linear_reducescatter_forward_gather_backward(
                    input_,
                    self.weight,
                    process_group=self.process_group,
                    dim=self.seq_parallel_dim,
                    ring=self.seq_parallel_mode == "ring",
                )
            else:
                output_parallel = F.linear(input_, self.weight)
                output = reduce_forward(output_parallel, self.process_group, fp8_communication=self.fp8_communication)

        if not self.skip_bias_add:
            if self.bias is not None:
                output = output + self.bias
            return output
        else:
            return output, self.bias


class PaddingLMHead(PaddingParallelModule):
    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = True,
        dtype: torch.dtype = None,
        device: torch.device = None,
        weight: Optional[Parameter] = None,
        bias_: Optional[Parameter] = None,
        make_vocab_size_divisible_by: int = 64,
        weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
        bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
    ):
        # Keep input parameters
        self.in_features = in_features
        self.out_features = out_features

        if out_features % make_vocab_size_divisible_by != 0:
            self.out_features = (
                out_features + make_vocab_size_divisible_by - (out_features % make_vocab_size_divisible_by)
            )
        if weight is None:
            factory_kwargs = {"device": device, "dtype": dtype}
            weight = Parameter(torch.empty(out_features, self.in_features, **factory_kwargs))
        else:
            weight.data = weight.data.to(device=device, dtype=dtype)

        if bias:
            if bias_ is None:
                self.bias = Parameter(torch.empty(out_features, **factory_kwargs))
            else:
                bias_.data = bias_.data.to(device=device, dtype=dtype)
        else:
            bias_ = None

        # resize embeddings
        super().__init__(self.out_features, out_features, weight, bias_)

        if weight is None:
            self.reset_parameters(weight_initializer, bias_initializer)

    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        fan_in, fan_out = self.in_features, self.out_features
        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
        if self.bias is not None:
            bias_initializer(self.bias, fan_in=fan_in)

    @staticmethod
    def from_native_module(
        module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], **kwargs
    ) -> PaddingParallelModule:
        r"""
        Convert a native PyTorch linear layer to a parallelized linear layer.
        """
        LazyInitContext.materialize(module)
        # get the attributes
        in_features = module.in_features
        out_features = module.out_features
        bias = module.bias is not None
        device = module.weight.device
        # ensure only one process group is passed

        lm_head_linear = PaddingLMHead(
            in_features=in_features,
            out_features=out_features,
            bias=bias,
            device=device,
            weight=module.weight,
            bias_=module.bias,
            **kwargs,
        )

        return lm_head_linear

    def forward(self, input: Tensor) -> Tensor:
        output = F.linear(input, self.weight, self.bias)
        output = output[..., : self.old_num_embeddings]
        return output


class VocabParallelLMHead1D(Linear1D_Col, PaddingParallelModule):
    r"""Linear layer with column parallelism.

    The linear layer is defined as :math:`Y = XA + b`. A is parallelized along
    its second dimension as :math:`A = [A_1, ..., A_p]`.

    Args:
        in_features (int): size of each input sample.
        out_features (int): size of each output sample.
        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
        dtype (`torch.dtype`): The dtype of parameters, defaults to None.
        device (`torch.device`): The device of parameters, defaults to None.
        process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None.
        gather_output (bool, optional): If true, call all-gather on output and make Y available
                    to all GPUs, otherwise, every GPU will have its output
                    which is :math:`Y_i = XA_i`, defaults to False
        seq_parallel (`bool`): If set to ``True``, it will use sequence parallel, defaults to False.
        skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
            which is preserved for kernel fusion, defaults to False
        weight_initializer (`typing.Callable`):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (`typing.Callable`):
            The initializer of bias, defaults to xavier uniform initializer.

    More details about ``initializer`` please refer to
    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = True,
        dtype: torch.dtype = None,
        device: torch.device = None,
        process_group: ProcessGroup = None,
        weight: Optional[Parameter] = None,
        bias_: Optional[Parameter] = None,
        make_vocab_size_divisible_by: int = 64,
        fp8_communication: bool = False,
        **kwargs,
    ):
        # create weight and bias
        if weight is None:
            factory_kwargs = {"device": device, "dtype": dtype}
            weight = Parameter(torch.empty(out_features, self.in_features, **factory_kwargs))
        if bias:
            if bias_ is None:
                bias_ = Parameter(torch.empty(out_features, **factory_kwargs))
        else:
            bias_ = None

        # calculate new vocab size
        self.tensor_parallel_size = dist.get_world_size(group=process_group)
        new_out_features = out_features
        multiple = make_vocab_size_divisible_by * self.tensor_parallel_size
        if out_features % multiple != 0:
            new_out_features = out_features + multiple - (out_features % multiple)

        super().__init__(
            in_features=in_features,
            out_features=new_out_features,
            bias=bias,
            device=device,
            process_group=process_group,
            weight=weight,
            bias_=bias_,
            **kwargs,
            new_num_embeddings=new_out_features,
            old_num_embeddings=out_features,
            fp8_communication=fp8_communication,
        )
        # get the length of valid embeddings
        tp_rank = dist.get_rank(process_group)
        partition_size = self.new_num_embeddings // dist.get_world_size(process_group)
        if self.old_num_embeddings >= (tp_rank + 1) * partition_size:
            self.num_valid_embeddings_local = partition_size
        elif self.old_num_embeddings >= tp_rank * partition_size:
            self.num_valid_embeddings_local = self.old_num_embeddings - tp_rank * partition_size
        else:
            self.num_valid_embeddings_local = 0

    @staticmethod
    def from_native_module(
        module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], **kwargs
    ) -> PaddingParallelModule:
        r"""
        Convert a native PyTorch linear layer to a parallelized linear layer.
        """
        LazyInitContext.materialize(module)
        # get the attributes
        in_features = module.in_features
        out_features = module.out_features
        bias = module.bias is not None
        device = module.weight.device

        lm_head_linear = VocabParallelLMHead1D(
            in_features=in_features,
            out_features=out_features,
            bias=bias,
            device=device,
            process_group=process_group,
            weight=module.weight,
            bias_=module.bias,
            **kwargs,
        )

        return lm_head_linear

    def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
        # get forward output
        if self.skip_bias_add:
            output, bias = super().forward(input_)
        else:
            output = super().forward(input_)

        # delete the padding of output
        if self.gather_output:
            output = output[..., : self.old_num_embeddings]
        else:
            output = output[..., : self.num_valid_embeddings_local]

        # return
        if self.skip_bias_add:
            return output, bias
        return output