parallel_state.py 22.8 KB
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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"""Model and data parallel groups."""

import torch
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from typing import Optional
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from .utils import GlobalMemoryBuffer

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# Intra-layer model parallel group that the current rank belongs to.
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_TENSOR_MODEL_PARALLEL_GROUP = None
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# Inter-layer model parallel group that the current rank belongs to.
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_PIPELINE_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra- and pipeline) that the current rank belongs to.
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_MODEL_PARALLEL_GROUP = None
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# Embedding group.
_EMBEDDING_GROUP = None
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# Position embedding group.
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_POSITION_EMBEDDING_GROUP = None
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# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None

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_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
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_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = None
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# These values enable us to change the mpu sizes on the fly.
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_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
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# A list of ranks that have a copy of the embedding.
_EMBEDDING_GLOBAL_RANKS = None

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# A list of ranks that have a copy of the position embedding.
_POSITION_EMBEDDING_GLOBAL_RANKS = None

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# A list of global ranks for each pipeline group to ease calculation of the source
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# rank when broadcasting from the first or last pipeline stage.
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_PIPELINE_GLOBAL_RANKS = None
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# A list of global ranks for each data parallel group to ease calculation of the source
# rank when broadcasting weights from src to all other data parallel ranks
_DATA_PARALLEL_GLOBAL_RANKS = None

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# Memory buffers to avoid dynamic memory allocation
_GLOBAL_MEMORY_BUFFER = None
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def initialize_model_parallel(
    tensor_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
    virtual_pipeline_model_parallel_size: Optional[int] = None,
    pipeline_model_parallel_split_rank: Optional[int] = None,
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    untie_embeddings_and_output_weights: bool = False,
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) -> None:
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    """
    Initialize model data parallel groups.

    Arguments:
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        tensor_model_parallel_size (int, default = 1):
            The number of GPUs to split individual tensors across.

        pipeline_model_parallel_size (int, default = 1):
            The number of tensor parallel GPU groups to split the
            Transformer layers across. For example, if
            tensor_model_parallel_size is 4 and
            pipeline_model_parallel_size is 2, the model will be split
            into 2 groups of 4 GPUs.

        virtual_pipeline_model_parallel_size (int, optional):
            The number of stages that each pipeline group will have,
            interleaving as necessary. If None, no interleaving is
            performed. For example, if tensor_model_parallel_size is 1,
            pipeline_model_parallel_size is 4,
            virtual_pipeline_model_parallel_size is 2, and there are
            16 transformer layers in the model, the model will be
            split into 8 stages with two layers each and each GPU
            would get 2 stages as such (layer number starting with 1):

            GPU 0: [1, 2] [9, 10]
            GPU 1: [3, 4] [11, 12]
            GPU 2: [5, 6] [13, 14]
            GPU 3: [7, 8] [15, 16]

        pipeline_model_parallel_split_rank (int, optional):
            For models with both an encoder and decoder, the rank in
            pipeline to switch between encoder and decoder (i.e. the
            first rank of the decoder). This allows the user to set
            the pipeline parallel size of the encoder and decoder
            independently. For example, if
            pipeline_model_parallel_size is 8 and
            pipeline_model_parallel_split_rank is 3, then ranks 0-2
            will be the encoder and ranks 3-7 will be the decoder.
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        untie_embeddings_and_output_weights: whether to use separate embedding and output layer.
                this affects the computation of embedding groups

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    Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
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    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
    the model pipeline. The present function will
    create 8 tensor model-parallel groups, 4 pipeline model-parallel groups
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    and 8 data-parallel groups as:
        8 data_parallel groups:
            [g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
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        8 tensor model-parallel groups:
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            [g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
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        4 pipeline model-parallel groups:
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            [g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15]
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    Note that for efficiency, the caller should make sure adjacent ranks
    are on the same DGX box. For example if we are using 2 DGX-1 boxes
    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
    ranks 8 to 15 belong to the second box.
    """
    # Get world size and rank. Ensure some consistencies.
    assert torch.distributed.is_initialized()
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    world_size: int = torch.distributed.get_world_size()

    if world_size % (tensor_model_parallel_size * pipeline_model_parallel_size) != 0:
        raise RuntimeError(
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            f"world_size ({world_size}) is not divisible by tensor_model_parallel_size "
            f"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size})"
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        )

    data_parallel_size: int = world_size // (tensor_model_parallel_size *
                                             pipeline_model_parallel_size)

    num_tensor_model_parallel_groups: int  = world_size // tensor_model_parallel_size
    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
    num_data_parallel_groups: int = world_size // data_parallel_size

    if virtual_pipeline_model_parallel_size is not None:
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        if not pipeline_model_parallel_size > 2:
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            raise RuntimeError("pipeline-model-parallel size should be greater than 2 with "
                               "interleaved schedule")
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        global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
        global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
        _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = 0
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        _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = virtual_pipeline_model_parallel_size
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    if pipeline_model_parallel_split_rank is not None:
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        global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
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        _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = pipeline_model_parallel_split_rank
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    rank = torch.distributed.get_rank()

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    # Build the data-parallel groups.
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    global _DATA_PARALLEL_GROUP
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    global _DATA_PARALLEL_GLOBAL_RANKS
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    assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized'
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    all_data_parallel_group_ranks = []
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    for i in range(pipeline_model_parallel_size):
        start_rank = i * num_pipeline_model_parallel_groups
        end_rank = (i + 1) * num_pipeline_model_parallel_groups
        for j in range(tensor_model_parallel_size):
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            ranks = range(start_rank + j, end_rank, tensor_model_parallel_size)
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            all_data_parallel_group_ranks.append(list(ranks))
            group = torch.distributed.new_group(ranks)
            if rank in ranks:
                _DATA_PARALLEL_GROUP = group
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                _DATA_PARALLEL_GLOBAL_RANKS = ranks
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    # Build the model-parallel groups.
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    global _MODEL_PARALLEL_GROUP
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    assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized'
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    for i in range(data_parallel_size):
        ranks = [data_parallel_group_ranks[i]
                 for data_parallel_group_ranks in all_data_parallel_group_ranks]
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        group = torch.distributed.new_group(ranks)
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        if rank in ranks:
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            _MODEL_PARALLEL_GROUP = group

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    # Build the tensor model-parallel groups.
    global _TENSOR_MODEL_PARALLEL_GROUP
    assert _TENSOR_MODEL_PARALLEL_GROUP is None, \
        'tensor model parallel group is already initialized'
    for i in range(num_tensor_model_parallel_groups):
        ranks = range(i * tensor_model_parallel_size,
                      (i + 1) * tensor_model_parallel_size)
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        group = torch.distributed.new_group(ranks)
        if rank in ranks:
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            _TENSOR_MODEL_PARALLEL_GROUP = group
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    # Build the pipeline model-parallel groups and embedding groups
    # (first and last rank in each pipeline model-parallel group).
    global _PIPELINE_MODEL_PARALLEL_GROUP
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    global _PIPELINE_GLOBAL_RANKS
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    assert _PIPELINE_MODEL_PARALLEL_GROUP is None, \
        'pipeline model parallel group is already initialized'
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    global _EMBEDDING_GROUP
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    global _EMBEDDING_GLOBAL_RANKS
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    assert _EMBEDDING_GROUP is None, 'embedding group is already initialized'
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    global _POSITION_EMBEDDING_GROUP
    global _POSITION_EMBEDDING_GLOBAL_RANKS
    assert _POSITION_EMBEDDING_GROUP is None, \
        'position embedding group is already initialized'
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    for i in range(num_pipeline_model_parallel_groups):
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        ranks = range(i, world_size, num_pipeline_model_parallel_groups)
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        group = torch.distributed.new_group(ranks)
        if rank in ranks:
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            _PIPELINE_MODEL_PARALLEL_GROUP = group
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            _PIPELINE_GLOBAL_RANKS = ranks
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        # Setup embedding group (to exchange gradients between
        # first and last stages).
        if len(ranks) > 1:
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            if untie_embeddings_and_output_weights:
                embedding_ranks = [ranks[0]]
            else:
                embedding_ranks = [ranks[0], ranks[-1]]
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            position_embedding_ranks = [ranks[0]]
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            if pipeline_model_parallel_split_rank is not None:
                if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:
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                    if untie_embeddings_and_output_weights:
                        embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]
                    else:
                        embedding_ranks = [ranks[0],
                                        ranks[pipeline_model_parallel_split_rank],
                                        ranks[-1]]
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                if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:
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                    position_embedding_ranks = [ranks[0],
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                                       ranks[pipeline_model_parallel_split_rank]]
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        else:
            embedding_ranks = ranks
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            position_embedding_ranks = ranks

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        group = torch.distributed.new_group(embedding_ranks)
        if rank in embedding_ranks:
            _EMBEDDING_GROUP = group
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        if rank in ranks:
            _EMBEDDING_GLOBAL_RANKS = embedding_ranks
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        group = torch.distributed.new_group(position_embedding_ranks)
        if rank in position_embedding_ranks:
            _POSITION_EMBEDDING_GROUP = group
        if rank in ranks:
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            _POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
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    # Initialize global memory buffer
    # This isn't really "parallel state" but there isn't another good place to
    # put this. If we end up with a more generic initialization of megatron-core
    # we could stick it there
    _set_global_memory_buffer()

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def model_parallel_is_initialized():
    """Check if model and data parallel groups are initialized."""
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    if _TENSOR_MODEL_PARALLEL_GROUP is None or \
        _PIPELINE_MODEL_PARALLEL_GROUP is None or \
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        _DATA_PARALLEL_GROUP is None:
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        return False
    return True


def get_model_parallel_group():
    """Get the model parallel group the caller rank belongs to."""
    assert _MODEL_PARALLEL_GROUP is not None, \
        'model parallel group is not initialized'
    return _MODEL_PARALLEL_GROUP


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def get_tensor_model_parallel_group():
    """Get the tensor model parallel group the caller rank belongs to."""
    assert _TENSOR_MODEL_PARALLEL_GROUP is not None, \
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        'intra_layer_model parallel group is not initialized'
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    return _TENSOR_MODEL_PARALLEL_GROUP
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def get_pipeline_model_parallel_group():
    """Get the pipeline model parallel group the caller rank belongs to."""
    assert _PIPELINE_MODEL_PARALLEL_GROUP is not None, \
        'pipeline_model parallel group is not initialized'
    return _PIPELINE_MODEL_PARALLEL_GROUP
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def get_data_parallel_group():
    """Get the data parallel group the caller rank belongs to."""
    assert _DATA_PARALLEL_GROUP is not None, \
        'data parallel group is not initialized'
    return _DATA_PARALLEL_GROUP


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def get_embedding_group():
    """Get the embedding group the caller rank belongs to."""
    assert _EMBEDDING_GROUP is not None, \
        'embedding group is not initialized'
    return _EMBEDDING_GROUP


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def get_position_embedding_group():
    """Get the position embedding group the caller rank belongs to."""
    assert _POSITION_EMBEDDING_GROUP is not None, \
        'position embedding group is not initialized'
    return _POSITION_EMBEDDING_GROUP


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def set_tensor_model_parallel_world_size(world_size):
    """Set the tensor model parallel size"""
    global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
    _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size
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def set_pipeline_model_parallel_world_size(world_size):
    """Set the pipeline model parallel size"""
    global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
    _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
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def get_tensor_model_parallel_world_size():
    """Return world size for the tensor model parallel group."""
    global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
    if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None:
        return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
    return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
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def get_pipeline_model_parallel_world_size():
    """Return world size for the pipeline model parallel group."""
    global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
    if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None:
        return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
    return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group())
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def set_tensor_model_parallel_rank(rank):
    """Set tensor model parallel rank."""
    global _MPU_TENSOR_MODEL_PARALLEL_RANK
    _MPU_TENSOR_MODEL_PARALLEL_RANK = rank
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def set_pipeline_model_parallel_rank(rank):
    """Set pipeline model parallel rank."""
    global _MPU_PIPELINE_MODEL_PARALLEL_RANK
    _MPU_PIPELINE_MODEL_PARALLEL_RANK = rank
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def set_pipeline_model_parallel_split_rank(rank):
    """Set pipeline model parallel split rank."""
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    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
    _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = rank
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def get_tensor_model_parallel_rank():
    """Return my rank for the tensor model parallel group."""
    global _MPU_TENSOR_MODEL_PARALLEL_RANK
    if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None:
        return _MPU_TENSOR_MODEL_PARALLEL_RANK
    return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
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def get_pipeline_model_parallel_rank():
    """Return my rank for the pipeline model parallel group."""
    global _MPU_PIPELINE_MODEL_PARALLEL_RANK
    if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
        return _MPU_PIPELINE_MODEL_PARALLEL_RANK
    return torch.distributed.get_rank(group=get_pipeline_model_parallel_group())
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def get_pipeline_model_parallel_split_rank():
    """Return pipeline model parallel split rank."""
    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
    return _PIPELINE_MODEL_PARALLEL_SPLIT_RANK

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def is_pipeline_first_stage(ignore_virtual=False):
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    """Return True if in the first pipeline model-parallel stage, False otherwise."""
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    if not ignore_virtual:
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        if get_virtual_pipeline_model_parallel_world_size() is not None and \
            get_virtual_pipeline_model_parallel_rank() != 0:
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            return False
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    return get_pipeline_model_parallel_rank() == 0
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def is_pipeline_last_stage(ignore_virtual=False):
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    """Return True if in the last pipeline model-parallel stage, False otherwise."""
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    if not ignore_virtual:
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        virtual_pipeline_model_parallel_world_size = \
            get_virtual_pipeline_model_parallel_world_size()
        if virtual_pipeline_model_parallel_world_size is not None and \
            get_virtual_pipeline_model_parallel_rank() != (
                virtual_pipeline_model_parallel_world_size - 1):
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            return False
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    return get_pipeline_model_parallel_rank() == (
        get_pipeline_model_parallel_world_size() - 1)
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def is_rank_in_embedding_group(ignore_virtual=False):
    """Return true if current rank is in embedding group, False otherwise."""
    rank = torch.distributed.get_rank()
    global _EMBEDDING_GLOBAL_RANKS
    if ignore_virtual:
        return rank in _EMBEDDING_GLOBAL_RANKS
    if rank in _EMBEDDING_GLOBAL_RANKS:
        if rank == _EMBEDDING_GLOBAL_RANKS[0]:
            return is_pipeline_first_stage(ignore_virtual=False)
        elif rank == _EMBEDDING_GLOBAL_RANKS[-1]:
            return is_pipeline_last_stage(ignore_virtual=False)
        else:
            return True
    return False


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def is_rank_in_position_embedding_group():
    """Return true if current rank is in position embedding group, False otherwise."""
    rank = torch.distributed.get_rank()
    global _POSITION_EMBEDDING_GLOBAL_RANKS
    return rank in _POSITION_EMBEDDING_GLOBAL_RANKS


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def is_pipeline_stage_before_split(rank=None):
    """Return True if pipeline stage executes encoder block for a model
    with both encoder and decoder."""
    if get_pipeline_model_parallel_world_size() == 1:
        return True
    if rank is None:
        rank = get_pipeline_model_parallel_rank()
    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
    if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None:
        return True
    if rank < _PIPELINE_MODEL_PARALLEL_SPLIT_RANK:
        return True
    return False


def is_pipeline_stage_after_split(rank=None):
    """Return True if pipeline stage executes decoder block for a model
    with both encoder and decoder."""
    if get_pipeline_model_parallel_world_size() == 1:
        return True
    if rank is None:
        rank = get_pipeline_model_parallel_rank()
    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
    if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None:
        return True
    if rank >= _PIPELINE_MODEL_PARALLEL_SPLIT_RANK:
        return True
    return False


def is_pipeline_stage_at_split():
    """Return true if pipeline stage executes decoder block and next
    stage executes encoder block for a model with both encoder and
    decoder."""
    rank = get_pipeline_model_parallel_rank()
    return is_pipeline_stage_before_split(rank) and \
            is_pipeline_stage_after_split(rank+1)


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def get_virtual_pipeline_model_parallel_rank():
    """Return the virtual pipeline-parallel rank."""
    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
    return _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK


def set_virtual_pipeline_model_parallel_rank(rank):
    """Set the virtual pipeline-parallel rank."""
    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
    _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = rank


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def get_virtual_pipeline_model_parallel_world_size():
    """Return the virtual pipeline-parallel world size."""
    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
    return _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE


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def get_tensor_model_parallel_src_rank():
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    """Calculate the global rank corresponding to the first local rank
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    in the tensor model parallel group."""
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    global_rank = torch.distributed.get_rank()
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    local_world_size = get_tensor_model_parallel_world_size()
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    return (global_rank // local_world_size) * local_world_size

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def get_data_parallel_src_rank():
    """Calculate the global rank corresponding to the first local rank
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    in the data parallel group."""
    assert _DATA_PARALLEL_GLOBAL_RANKS is not None, \
        "Data parallel group is not initialized"
    return _DATA_PARALLEL_GLOBAL_RANKS[0]
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def get_pipeline_model_parallel_first_rank():
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    """Return the global rank of the first process in the pipeline for the
    current tensor parallel group"""
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    assert _PIPELINE_GLOBAL_RANKS is not None, \
        "Pipeline parallel group is not initialized"
    return _PIPELINE_GLOBAL_RANKS[0]

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def get_pipeline_model_parallel_last_rank():
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    """Return the global rank of the last process in the pipeline for the
    current tensor parallel group"""
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    assert _PIPELINE_GLOBAL_RANKS is not None, \
        "Pipeline parallel group is not initialized"
    last_rank_local = get_pipeline_model_parallel_world_size() - 1
    return _PIPELINE_GLOBAL_RANKS[last_rank_local]
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def get_pipeline_model_parallel_next_rank():
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    """Return the global rank that follows the caller in the pipeline"""
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    assert _PIPELINE_GLOBAL_RANKS is not None, \
        "Pipeline parallel group is not initialized"
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    rank_in_pipeline = get_pipeline_model_parallel_rank()
    world_size = get_pipeline_model_parallel_world_size()
    return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size]

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def get_pipeline_model_parallel_prev_rank():
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    """Return the global rank that preceeds the caller in the pipeline"""
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    assert _PIPELINE_GLOBAL_RANKS is not None, \
        "Pipeline parallel group is not initialized"
    rank_in_pipeline = get_pipeline_model_parallel_rank()
    world_size = get_pipeline_model_parallel_world_size()
    return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size]
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def get_data_parallel_world_size():
    """Return world size for the data parallel group."""
    return torch.distributed.get_world_size(group=get_data_parallel_group())


def get_data_parallel_rank():
    """Return my rank for the data parallel group."""
    return torch.distributed.get_rank(group=get_data_parallel_group())

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def _set_global_memory_buffer():
    """Initialize global buffer"""
    global _GLOBAL_MEMORY_BUFFER
    assert _GLOBAL_MEMORY_BUFFER is None, 'global memory buffer is already initialized'
    _GLOBAL_MEMORY_BUFFER = GlobalMemoryBuffer()

def get_global_memory_buffer():
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    """Return the global GlobalMemoryBuffer object"""
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    assert _GLOBAL_MEMORY_BUFFER is not None, 'global memory buffer is not initialized'
    return _GLOBAL_MEMORY_BUFFER


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def destroy_model_parallel():
    """Set the groups to none."""
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    global _MODEL_PARALLEL_GROUP
    _MODEL_PARALLEL_GROUP = None
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    global _TENSOR_MODEL_PARALLEL_GROUP
    _TENSOR_MODEL_PARALLEL_GROUP = None
    global _PIPELINE_MODEL_PARALLEL_GROUP
    _PIPELINE_MODEL_PARALLEL_GROUP = None
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    global _DATA_PARALLEL_GROUP
    _DATA_PARALLEL_GROUP = None
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    global _EMBEDDING_GROUP
    _EMBEDDING_GROUP = None
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Vijay Korthikanti committed
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    global _POSITION_EMBEDDING_GROUP
    _POSITION_EMBEDDING_GROUP = None
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    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
    _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
    _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
    global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
    _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
    global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
    _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
    global _MPU_TENSOR_MODEL_PARALLEL_RANK
    _MPU_TENSOR_MODEL_PARALLEL_RANK = None
    global _MPU_PIPELINE_MODEL_PARALLEL_RANK
    _MPU_PIPELINE_MODEL_PARALLEL_RANK = None
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    global _GLOBAL_MEMORY_BUFFER
    _GLOBAL_MEMORY_BUFFER = None