ColossalAI/colossalai/nn/layer/parallel_3d/layers.py

import math
from typing import Callable

import torch
import torch.nn as nn
import torch.nn.functional as F
from colossalai.communication import all_reduce, broadcast
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
from colossalai.context import ParallelMode, seed
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.nn import init as init
from colossalai.nn.layer.base_layer import ParallelLayer
from colossalai.registry import LAYERS
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch.nn import Parameter

from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
from ._operation import layernorm_3d, linear_3d, classifier_3d, split_tensor_3d
from ._operation import all_gather_tensor_3d, reduce_scatter_tensor_3d, broadcast_weight_3d_from_diagonal
from ._utils import get_depth_from_env, get_last_group, get_parallel_mode_from_env, swap_in_out_group


@LAYERS.register_module
class LayerNorm3D(ParallelLayer):
    r"""
    Layer Normalization for 3D parallelism

    :param normalized_shape: input shape from an expected input of size.
    :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
    \times \ldots \times \text{normalized_shape}[-1]]`
        If a single integer is used, it is treated as a singleton list, and this module will
        normalize over the last dimension which is expected to be of that specific size.
    :type normalized_shape: int
    :param eps: a value added to the denominator for numerical stability, defaults to 1e-12
    :type eps: float, optional
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    """

    def __init__(self, normalized_shape: int, eps: float = 1e-12, dtype=None):

        super().__init__()
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
        self.depth = get_depth_from_env()
        self.normalized_shape = normalized_shape
        self.normalized_shape_per_partition = divide(normalized_shape, self.depth)

        self.weight = Parameter(
            torch.ones(self.normalized_shape_per_partition, device=get_current_device(), dtype=dtype))
        self.bias = Parameter(torch.zeros(self.normalized_shape_per_partition, device=get_current_device(),
                                          dtype=dtype))
        self.variance_epsilon = eps
        self._set_tensor_parallel_attributes()

    def _set_tensor_parallel_attributes(self) -> None:
        set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
        set_tensor_parallel_attribute_by_partition(self.bias, self.depth)

    def reset_parameters(self) -> None:
        init.zeros_()(self.bias)
        init.ones_()(self.weight)

    def forward(self, input_: Tensor) -> Tensor:
        return layernorm_3d(input_, self.weight, self.bias, self.normalized_shape, self.variance_epsilon,
                            self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode)


@LAYERS.register_module
class Linear3D(ParallelLayer):
    """
    Linear layer for 3D parallelism

    :param in_features: size of each input sample
    :type in_features: int
    :param out_features: size of each output sample
    :type out_features: int
    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to True
    :type bias: bool, optional
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
    :type weight_initializer: typing.Callable, optional
    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
    :type bias_initializer: typing.Callable, optional
    """

    def __init__(self,
                 in_features: int,
                 out_features: int,
                 bias: bool = True,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
        self.depth = get_depth_from_env()
        self.in_features_per_partition = divide(in_features, self.depth)
        self.out_features_per_partition = divide(out_features, self.depth**2)
        self.bias_features_per_partition = divide(out_features, self.depth)

        self.weight = Parameter(
            torch.empty(self.in_features_per_partition,
                        self.out_features_per_partition,
                        device=get_current_device(),
                        dtype=dtype))
        if bias:
            self.bias = Parameter(
                torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype))
        else:
            self.bias = None

        self.reset_parameters(weight_initializer, bias_initializer)
        self._set_tensor_parallel_attributes()
        swap_in_out_group()

    def _set_tensor_parallel_attributes(self) -> None:
        set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3)
        if self.bias is not None:
            set_tensor_parallel_attribute_by_partition(self.bias, self.depth)

    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        with seed(ParallelMode.TENSOR):
            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)
                weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
                output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
                broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
                broadcast(self.bias, output_src_rank, self.output_parallel_mode)

    def forward(self, input_: Tensor) -> Tensor:
        return linear_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
                         self.output_parallel_mode)


@LAYERS.register_module
class Classifier3D(ParallelLayer):
    """
    Classifier for 3D parallelism

    :param in_features: size of each input sample
    :type in_features: int
    :param num_classes: number of classes
    :type num_classes: int
    :param weight: weight of the classifier, defaults to True
    :type weight: torch.nn.Parameter, optional
    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to True
    :type bias: bool, optional
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
    :type weight_initializer: typing.Callable, optional
    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
    :type bias_initializer: typing.Callable, optional
    """

    def __init__(self,
                 in_features: int,
                 num_classes: int,
                 weight: Parameter = None,
                 bias: bool = True,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
        super().__init__()
        self.in_features = in_features
        self.num_classes = num_classes
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
        self.depth = get_depth_from_env()
        self.in_features_per_partition = divide(in_features, self.depth)

        if weight is not None:
            self.weight = weight
            self.has_weight = False
        else:
            self.weight = Parameter(
                torch.empty(self.num_classes, self.in_features_per_partition, device=get_current_device(), dtype=dtype))
            self.has_weight = True
        if bias:
            self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
        else:
            self.bias = None

        self.reset_parameters(weight_initializer, bias_initializer)
        self._set_tensor_parallel_attributes()

    def _set_tensor_parallel_attributes(self) -> None:
        if self.has_weight:
            set_tensor_parallel_attribute_by_partition(self.weight, self.depth)

    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        with seed(ParallelMode.TENSOR):
            fan_in, fan_out = self.in_features, self.num_classes
            weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
            output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
            input_src_rank = gpc.get_ranks_in_group(self.input_parallel_mode)[0]

            if self.has_weight:
                weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
                broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)

            if self.bias is not None:
                bias_initializer(self.bias, fan_in=fan_in)
                broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
                broadcast(self.bias, output_src_rank, self.output_parallel_mode)
                broadcast(self.bias, input_src_rank, self.input_parallel_mode)

    def forward(self, input_: Tensor) -> Tensor:
        return classifier_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
                             self.output_parallel_mode)


@LAYERS.register_module
class VocabParallelClassifier3D(ParallelLayer):
    """
    Vocab parallel classifier layer for 2D parallelism

    :param in_features: size of each input sample
    :type in_features: int
    :param num_classes: number of classes
    :type num_classes: int
    :param weight: weight of the classifier, defaults to True
    :type weight: torch.nn.Parameter, optional
    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
    :type bias: bool, optional
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
    :type weight_initializer: typing.Callable, optional
    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
    :type bias_initializer: typing.Callable, optional
    """

    def __init__(self,
                 in_features: int,
                 num_classes: int,
                 weight: Parameter = None,
                 bias: bool = True,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
        super().__init__()
        self.in_features = in_features
        self.num_classes = num_classes
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
        self.depth = get_depth_from_env()
        self.in_features_per_partition = divide(in_features, self.depth)
        self.out_features_per_partition = divide(num_classes, self.depth**2)
        self.bias_features_per_partition = divide(num_classes, self.depth)

        if weight is not None:
            self.weight = weight
            self.has_weight = False
        else:
            self.weight = Parameter(
                torch.empty(self.out_features_per_partition,
                            self.in_features_per_partition,
                            device=get_current_device(),
                            dtype=dtype))
            self.has_weight = True
        if bias:
            self.bias = Parameter(
                torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype))
        else:
            self.bias = None

        self.reset_parameters(weight_initializer, bias_initializer)
        self._set_tensor_parallel_attributes()
        swap_in_out_group()
        env.vocab_parallel = True

    def _set_tensor_parallel_attributes(self) -> None:
        if self.has_weight:
            set_tensor_parallel_attribute_by_partition(self.weight, self.depth**2)
        if self.bias is not None:
            set_tensor_parallel_attribute_by_partition(self.bias, self.depth)

    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
        with seed(ParallelMode.TENSOR):
            fan_in, fan_out = self.in_features, self.num_classes

            if self.has_weight:
                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)
                weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
                output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
                broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
                broadcast(self.bias, output_src_rank, self.output_parallel_mode)

    def forward(self, input_: Tensor) -> Tensor:
        return linear_3d(input_, self.weight.transpose(0, 1), self.bias, self.input_parallel_mode,
                         self.weight_parallel_mode, self.output_parallel_mode)


@LAYERS.register_module
class PatchEmbedding3D(ParallelLayer):
    """
    2D Image to Patch Embedding

    :param img_size: image size
    :type img_size: int
    :param patch_size: patch size
    :type patch_size: int
    :param in_chans: number of channels of input image
    :type in_chans: int
    :param embed_size: size of embedding
    :type embed_size: int
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    :param flatten: whether to flatten output tensor, defaults to True
    :type flatten: bool, optional
    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
    :type weight_initializer: typing.Callable, optional
    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
    :type bias_initializer: typing.Callable, optional
    :param position_embed_initializer: The intializer of position embedding, defaults to zero
    :type position_embed_initializer: typing.Callable, optional
    """

    def __init__(self,
                 img_size: int,
                 patch_size: int,
                 in_chans: int,
                 embed_size: int,
                 flatten: bool = True,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                 position_embed_initializer: Callable = init.zeros_()):
        super().__init__()
        self.depth = get_depth_from_env()
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
        self.patch_size = to_2tuple(patch_size)
        grid_size = to_2tuple(img_size // patch_size)
        num_patches = grid_size[0] * grid_size[1]
        self.embed_size = embed_size
        embed_size_per_partition = divide(embed_size, self.depth)
        self.flatten = flatten

        self.weight = nn.Parameter(
            torch.empty((embed_size_per_partition, in_chans, *self.patch_size),
                        device=get_current_device(),
                        dtype=dtype))
        self.bias = nn.Parameter(torch.empty(embed_size_per_partition, device=get_current_device(), dtype=dtype))

        self.cls_token = nn.Parameter(
            torch.zeros((1, 1, embed_size_per_partition), device=get_current_device(), dtype=dtype))
        self.pos_embed = nn.Parameter(
            torch.zeros((1, num_patches + 1, embed_size_per_partition), device=get_current_device(), dtype=dtype))

        self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
        self._set_tensor_parallel_attributes()

    def _set_tensor_parallel_attributes(self) -> None:
        set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
        set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
        set_tensor_parallel_attribute_by_partition(self.cls_token, self.depth)
        set_tensor_parallel_attribute_by_partition(self.pos_embed, self.depth)

    def _sync_grad_hook(self, grad) -> Tensor:
        grad = all_reduce(grad.clone(), self.input_parallel_mode)
        grad = all_reduce(grad, self.weight_parallel_mode)
        return grad

    def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer) -> None:
        with seed(ParallelMode.TENSOR):
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            fan_out = self.embed_size
            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
            bias_initializer(self.bias, fan_in=fan_in)
            position_embed_initializer(self.pos_embed)

        weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
        input_src_rank = gpc.get_ranks_in_group(self.input_parallel_mode)[0]
        broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
        broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
        broadcast(self.pos_embed, weight_src_rank, self.weight_parallel_mode)
        broadcast(self.weight, input_src_rank, self.input_parallel_mode)
        broadcast(self.bias, input_src_rank, self.input_parallel_mode)
        broadcast(self.pos_embed, input_src_rank, self.input_parallel_mode)

        self.weight.register_hook(self._sync_grad_hook)
        self.bias.register_hook(self._sync_grad_hook)
        self.cls_token.register_hook(self._sync_grad_hook)
        self.pos_embed.register_hook(self._sync_grad_hook)

    def forward(self, input_: Tensor) -> Tensor:
        input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
        input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
        output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
        if self.flatten:
            output = output.flatten(2).transpose(1, 2)    # BCHW -> BNC

        cls_token = self.cls_token.expand(output.shape[0], -1, -1)
        output = torch.cat((cls_token, output), dim=1)
        output = output + self.pos_embed

        return output


@LAYERS.register_module
class Embedding3D(ParallelLayer):
    """
    Embedding for 3D parallelism

    :param num_embeddings: number of embeddings
    :type num_embeddings: int
    :param embedding_dim: dimension of embedding
    :type embedding_dim: int
    :param padding_idx: index of padding, defaults to None
    :type padding_idx: int, optional
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    :param weight_initializer: The intializer of weight, defaults to normal initializer
    :type weight_initializer: typing.Callable, optional
    :param args: Args used in F.embedding
    :param kwargs: Kwargs used in F.embedding
    """

    def __init__(self,
                 num_embeddings: int,
                 embedding_dim: int,
                 padding_idx: int = None,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.normal_(),
                 *args,
                 **kwargs):
        super().__init__()
        self.depth = get_depth_from_env()
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)

        self.num_embeddings = num_embeddings
        self.embed_dim = embedding_dim
        embed_dim_per_partition = divide(embedding_dim, self.depth)
        self.padding_idx = padding_idx
        self.embed_args = args
        self.embed_kwargs = kwargs

        self.weight = nn.Parameter(
            torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))

        self.reset_parameters(weight_initializer)
        self._set_tensor_parallel_attributes()

    def _set_tensor_parallel_attributes(self) -> None:
        set_tensor_parallel_attribute_by_partition(self.weight, self.depth)

    def reset_parameters(self, weight_initializer) -> None:
        with seed(ParallelMode.TENSOR):
            fan_in, fan_out = self.num_embeddings, self.embed_dim
            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
            self._fill_padding_idx_with_zero()
        weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
        broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None:
            with torch.no_grad():
                self.weight[self.padding_idx].fill_(0)

    def forward(self, input_: Tensor) -> Tensor:
        input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
        input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
        weight = broadcast_weight_3d_from_diagonal(self.weight, self.input_parallel_mode, self.weight_parallel_mode,
                                                   self.output_parallel_mode)
        output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)

        return output


@LAYERS.register_module
class VocabParallelEmbedding3D(torch.nn.Module):
    """Embedding parallelized in the vocabulary dimension.

    :param num_embeddings: number of embeddings
    :type num_embeddings: int
    :param embedding_dim: dimension of embedding
    :type embedding_dim: int
    :param padding_idx: index of padding, defaults to None
    :type padding_idx: int, optional
    :param dtype: The dtype of parameters, defaults to None
    :type dtype: torch.dtype, optional
    :param weight_initializer: The intializer of weight, defaults to normal initializer
    :type weight_initializer: typing.Callable, optional
    :param args: Args used in F.embedding
    :param kwargs: Kwargs used in F.embedding
    """

    def __init__(self,
                 num_embeddings: int,
                 embedding_dim: int,
                 padding_idx: int = None,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.normal_(),
                 *args,
                 **kwargs):
        super().__init__()
        self.num_embeddings = num_embeddings
        self.embed_dim = embedding_dim
        self.padding_idx = padding_idx
        self.embed_args = args
        self.embed_kwargs = kwargs

        self.depth = get_depth_from_env()
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
        self.num_embeddings_per_partition = divide(self.num_embeddings, self.depth**2)
        self.embed_dim_per_partition = divide(self.embed_dim, self.depth)
        vocab_parallel_rank = gpc.get_local_rank(self.input_parallel_mode)
        self.vocab_start_index = vocab_parallel_rank * self.num_embeddings_per_partition * self.depth
        self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition * self.depth

        self.weight = Parameter(
            torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
                        device=get_current_device(),
                        dtype=dtype))

        self.reset_parameters(weight_initializer)
        self._set_tensor_parallel_attributes()
        env.vocab_parallel = True

    def _set_tensor_parallel_attributes(self):
        set_tensor_parallel_attribute_by_partition(self.weight, self.depth**2)

    def reset_parameters(self, weight_initializer) -> None:
        with seed(ParallelMode.TENSOR):
            fan_in, fan_out = self.num_embeddings, self.embed_dim
            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
            self._fill_padding_idx_with_zero()

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None and \
                self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
            with torch.no_grad():
                self.weight[self.padding_idx - self.vocab_start_index].fill_(0)

    def forward(self, input_: Tensor) -> Tensor:
        input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)

        input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
        masked_input = input_.clone() - self.vocab_start_index
        masked_input[input_mask] = 0

        weight = all_gather_tensor_3d(self.weight, 0, self.weight_parallel_mode)

        output_parallel = F.embedding(masked_input, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)

        output_parallel[input_mask, :] = 0.
        output = reduce_scatter_tensor_3d(output_parallel, 0, self.input_parallel_mode)

        return output