【Megatron-DeepSpeed】张量并行工具代码mpu详解(二)：Collective通信操作的封装mappings

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【深度学习】【分布式训练】一文捋顺千亿模型训练技术：流水线并行、张量并行和3D并行
【Megatron-DeepSpeed】张量并行工具代码mpu详解(四)：张量并行版Embedding层及交叉熵的实现及测试
【Megatron-DeepSpeed】张量并行工具代码mpu详解(三)：张量并行层的实现及测试
【Megatron-DeepSpeed】张量并行工具代码mpu详解(一)：并行环境初始化
【Megatron-DeepSpeed】张量并行工具代码mpu详解(二)：Collective通信操作的封装mappings
【深度学习】【分布式训练】DeepSpeed：AllReduce与ZeRO-DP
【深度学习】混合精度训练与显存分析
【深度学习】【分布式训练】Collective通信操作及Pytorch示例
【自然语言处理】【大模型】大语言模型BLOOM推理工具测试

​ Megatron-DeepSpeed是DeepSpeed版本的NVIDIA Megatron-LM。像BLOOM、GLM-130B等主流大模型都是基于Megatron-DeepSpeed开发的。这里以BLOOM版本的Megetron-DeepSpeed为例，介绍其张量并行代码mpu的细节(位于megatron/mpu下)。

​ 相关原理知识建议阅读：

• 【深度学习】【分布式训练】Collective通信操作及Pytorch示例
• 【深度学习】【分布式训练】一文捋顺千亿模型训练技术：流水线并行、张量并行和3D并行
• 【深度学习】【分布式训练】DeepSpeed：AllReduce与ZeRO-DP

​ 强烈建议阅读，不然会影响本文的理解：

• 【Megatron-DeepSpeed】张量并行工具代码mpu详解(一)：并行环境初始化

阅读建议：

1. 本文仅会解析核心代码，并会不介绍所有代码；
2. 本文会提供一些测试脚本来展现各部分代码的功能；
3. 建议实际动手实操来加深理解；
4. 建议对Collective通信以及分布式模型训练有一定理解，再阅读本文；

一、总览

​ mpu目录下核心文件有：

• initialize.py：负责数据并行组、张量并行组和流水线并行组的初始化，以及获取与各类并行组相关的信息；
• data.py：实现张量并行中的数据广播功能；
• cross_entropy.py：张量并行版本的交叉熵；
• layers.py：并行版本的Embedding层，以及列并行线性层和行并行线性层；
• mappings.py：用于张量并行的通信操作；

二、代码实现及测试

1. _reduce

源代码

​ _reduce提供在整个张量并行组进行All-Reduce的功能，函数定义如下 ：

def _reduce(input_):
    """
    在模型并行组上对输入张量执行All-reduce.
    """
    if get_tensor_model_parallel_world_size()==1:
        return input_

    # All-reduce.
    torch.distributed.all_reduce(input_, group=get_tensor_model_parallel_group())

    return input_

测试代码

​ 测试遵循文章【Megatron-DeepSpeed】张量并行工具代码mpu详解(一)：并行环境初始化 中的设置，张量并行度为2，且流水线并行度为2。则张量并行组为：[Rank0, Rank1],[Rank2, Rank3],[Rank4,Rank5],[Rank6,Rank7]。

def test_reduce():
    print_separator(f'> Test _reduce')
    global_rank = torch.distributed.get_rank()
    # global_rank为1时，则会生成张量tensor([1])
    tensor = torch.Tensor([global_rank]).to(torch.device("cuda", global_rank))
    print(f"> Before reduce: {tensor}")
    # 保证reduce前后的输出不混乱
    torch.distributed.barrier()
    # reduce操作
    # 期望结果：[Rank0, Rank1]为一组，经过reduce后均为tensor([1])
    # 期望结果：[Rank6, Rank7]为一组，经过reduce后均为tensor([13])
    mappings._reduce(tensor)
    print(f"> After reduce: {tensor}")

测试结果

2. _gather

源代码

​ 收集张量并行组中的张量，并按照最后一维度拼接.

def _gather(input_):
    """
    gather张量并按照最后一维度拼接.
    """

    world_size = get_tensor_model_parallel_world_size()
    
    if world_size==1:
        return input_
    # 最后一维的索引
    last_dim = input_.dim() - 1
    # 张量并行组中的rank
    rank = get_tensor_model_parallel_rank()
    # 初始化空张量列表，用于存储收集来的张量
    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
    tensor_list[rank] = input_
    torch.distributed.all_gather(tensor_list, input_, group=get_tensor_model_parallel_group())
    # 拼接
    output = torch.cat(tensor_list, dim=last_dim).contiguous()
    return output

测试代码

​ 实验设置同上。

def test_gather():
    print_separator(f'> Test _gather')
    global_rank = torch.distributed.get_rank()
    # global_rank为1时，则会生成张量tensor([1])
    tensor = torch.Tensor([global_rank]).to(torch.device("cuda", global_rank))
    print(f"> Before gather: {tensor}\n", end="")
    torch.distributed.barrier()
    # 期望结果：[Rank0, Rank1]为一组，经过gather后均为tensor([0., 1.])
    gather_tensor = mappings._gather(tensor)
    print(f"> After gather: {gather_tensor}\n", end="")

测试结果

3. _split

源代码

沿最后一维分割张量，并保留对应rank的分片.

def _split(input_):
    """
    沿最后一维分割张量，并保留对应rank的分片.
    """

    world_size = get_tensor_model_parallel_world_size()
    if world_size==1:
        return input_
    # 按world_size分割输入张量input_
    input_list = split_tensor_along_last_dim(input_, world_size)

    # Note: torch.split does not create contiguous tensors by default.
    rank = get_tensor_model_parallel_rank()
    output = input_list[rank].contiguous()
    
    return output

测试代码

​ 测试设置同上。

def test_split():
    print_separator(f'> Test _split')
    global_rank = torch.distributed.get_rank()
    # 在实验设置下为tp_world_size=2
    tp_world_size = mpu.get_tensor_model_parallel_world_size()
    # 在实验设置下tensor=[0,1]
    tensor = torch.Tensor(list(range(tp_world_size))).to(torch.device("cuda", global_rank))
    print(f"> Before split: {tensor}\n", end="")
    torch.distributed.barrier()
    # 期望结果：Rank0,Rank2,Rank4,Rank6持有张量tensor([0])
    # 期望结果：Rank1,Rank3,Rank5,Rank7持有张量tensor([1])
    split_tensor = mappings._split(tensor)
    print(f"> After split: {split_tensor}\n", end="")

测试结果

4. copy_to_tensor_model_parallel_region

源代码

• 前向传播时，不进行任何操作
• 反向传播时，对相同张量组中所有对input_的梯度求和

class _CopyToModelParallelRegion(torch.autograd.Function):
    @staticmethod
    def symbolic(graph, input_):
        return input_
    
    @staticmethod
    def forward(ctx, input_): # 前向传播时，不进行任何操作
        return input_

    @staticmethod
    def backward(ctx, grad_output): # 反向传播时，对同张量并行组的梯度进行求和
        return _reduce(grad_output)

def copy_to_tensor_model_parallel_region(input_):
    return _CopyToModelParallelRegion.apply(input_)

测试代码

​ 测试设置同上。本次实验中，会分别使用copy和非copy的张量来求梯度，展示其区别。

def test_copy_to_tensor_model_parallel_region():
    print_separator(f'> Test copy_to_tensor_model_region^S')
    global_rank = torch.distributed.get_rank()
    # global_rank为1时，则会生成张量tensor([1])
    tensor = Parameter(torch.Tensor([global_rank]).to(torch.device("cuda", global_rank)))
    loss = global_rank * tensor
    loss.backward()
    # 非copy的tensor梯度期望结果为，Ranki的梯度为i
    print(f"> No copy grad: {tensor.grad}\n", end="")
    torch.distributed.barrier()
    tensor.grad = None
    # 使用copy_to_tensor_model_parallel_region对tensor进行操作
    # 该操作不会影响前向传播，仅影响反向传播
    tensor_parallel = mappings.copy_to_tensor_model_parallel_region(tensor)
    # 例：对于rank=5，则loss=5*x，其反向传播的梯度为5；依次类推
    loss_parallel = global_rank * tensor_parallel
    loss_parallel.backward()
    torch.distributed.barrier()
    # 例：张量组[Rank6, Rank7]的期望梯度均为13
    print(f"> Copy grad: {tensor.grad}\n", end="")

测试结果

5. reduce_from_tensor_model_parallel_region

源代码

• 前向传播时，将同张量并行组的输入input_进行allreduce；
• 反向传播时，直接返回input_的梯度；

class _ReduceFromModelParallelRegion(torch.autograd.Function):
    @staticmethod
    def symbolic(graph, input_):
        return _reduce(input_)
    
    @staticmethod
    def forward(ctx, input_): # 前向传播时，对张量并行组中的输入进行allreduce
        return _reduce(input_)

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output
    
def reduce_from_tensor_model_parallel_region(input_):
    return _ReduceFromModelParallelRegion.apply(input_)

测试代码

​ 测试设置同上。

​ 以张量并行组[Rank6, Rank7]为例，$loss=2\ast (6\ast x_6+7\ast x_7)$。所以，前向传播的结果为$2\ast (6\ast 6+7\ast 7)=170$。Rank6的反向传播梯度为12，Rank7的反向传播梯度为14。

def test_reduce_from_tensor_model_parallel_region():
    print_separator(f"> Test reduce_from_tensor_model_parallel_region")
    global_rank = torch.distributed.get_rank()
    # global_rank为1时，则会生成张量tensor([1])
    tensor1 = Parameter(torch.Tensor([global_rank]).to(torch.device("cuda", global_rank)))
    tensor2 = global_rank * tensor1
    tensor_parallel = mappings.reduce_from_tensor_model_parallel_region(tensor2)
    loss = 2 * tensor_parallel
    loss.backward()
    print(f"> value: {tensor1.data}\n", end="")
    print(f"> grad: {tensor1.grad}\n", end="")

测试结果

6. scatter_to_tensor_model_parallel_region

源代码

• 前向传播时，将输入input_分片至同张量并行组的不同进程中；
• 反向传播时，将同张量并行组的梯度收集起来并拼接；

class _ScatterToModelParallelRegion(torch.autograd.Function):
    """
    分割输入，仅保留对应rank的块。
    """
    @staticmethod
    def symbolic(graph, input_):
        return _split(input_)

    @staticmethod
    def forward(ctx, input_): # 切分输入
        return _split(input_)

    @staticmethod
    def backward(ctx, grad_output): # 收集梯度
        return _gather(grad_output)

def scatter_to_tensor_model_parallel_region(input_):
    return _ScatterToModelParallelRegion.apply(input_)

测试代码

​ 测试设置同上。

​ 以张量并行组[Rank6, Rank7]为例，Rank6的梯度为6，Rank7的梯度为7。scatter_to_tensor_model_parallel_region的backward过程会收集两者的梯度，因此Rank6和Rank7的梯度均为tensor([6.,7.])。

def test_scatter_to_tensor_model_parallel_region():
    print_separator(f'> Test scatter_to_tensor_model_parallel_region')
    global_rank = torch.distributed.get_rank()
    tp_world_size = mpu.get_tensor_model_parallel_world_size()
    # tensor = [1,2]
    tensor = Parameter(torch.Tensor(list(range(1, tp_world_size+1))).to(torch.device("cuda", global_rank)))
    # split之后, Rank0、Rank2、Rank4、Rank6为tensor([1]), 其余Rank为tensor([2])
    tensor_split = mappings.scatter_to_tensor_model_parallel_region(tensor)
    loss = global_rank * tensor_split
    loss.backward()
    print(f"> Before split: {tensor}\n", end="")
    torch.distributed.barrier()
    print(f"> After split: {tensor_split}\n", end="")
    torch.distributed.barrier()
    print(f"> Grad: {tensor.grad}\n", end="")

测试结果

7. gather_from_tensor_model_parallel_region

源代码

• 前向传播时，将同张量并行组的input_收集在一起并进行拼接；
• 反向传播时，将梯度分片至同张量并行组的不同进程中；

class _GatherFromModelParallelRegion(torch.autograd.Function):
    """
    收集张量并行组的张量并拼接
    """
    @staticmethod
    def symbolic(graph, input_):
        return _gather(input_)
    
    @staticmethod
    def forward(ctx, input_): # 前向传播时，相同张量并行组gather在一起
        return _gather(input_)

    @staticmethod
    def backward(ctx, grad_output): # 反向传播时，将张量split至张量组中的机器
        return _split(grad_output)

测试代码

测试设置同上。

def test_gather_from_tensor_model_parallel_region():
    print_separator(f'> Test gather_from_tensor_model_parallel_region')
    global_rank = torch.distributed.get_rank()
    # tp_world_size = mpu.get_tensor_model_parallel_world_size()
    tensor = Parameter(torch.Tensor([global_rank]).to(torch.device("cuda", global_rank)))
    print(f"> Before gather: {tensor}\n", end="")
    torch.distributed.barrier()
    gather_tensor = mappings.gather_from_tensor_model_parallel_region(tensor)
    print(f"> After gather: {gather_tensor.data}\n", end="")
    loss = (global_rank * gather_tensor).sum()
    loss.backward()
    print(f"> Grad: {tensor.grad}\n", end="")

测试结果

三、完整测试脚本

​ 测试采用8张显卡。下面是完整的测试脚本：

# test_mappings.py
import sys
sys.path.append("..")

from torch.nn.parameter import Parameter
from commons import print_separator
from commons import initialize_distributed
import megatron.mpu.mappings as mappings
import megatron.mpu as mpu
import torch

def test_reduce():
    print_separator(f'> Test _reduce')
    global_rank = torch.distributed.get_rank()
    tensor = torch.Tensor([global_rank]).to(torch.device("cuda", global_rank))
    print(f"> Before reduce: {tensor}\n", end="")
    torch.distributed.barrier()
    mappings._reduce(tensor)
    print(f"> After reduce: {tensor}\n", end="")

def test_gather():
    print_separator(f'> Test _gather')
    global_rank = torch.distributed.get_rank()
    tensor = torch.Tensor([global_rank]).to(torch.device("cuda", global_rank))
    print(f"> Before gather: {tensor}\n", end="")
    torch.distributed.barrier()
    gather_tensor = mappings._gather(tensor)
    print(f"> After gather: {gather_tensor}\n", end="")

def test_split():
    print_separator(f'> Test _split')
    global_rank = torch.distributed.get_rank()
    tp_world_size = mpu.get_tensor_model_parallel_world_size()
    tensor = torch.Tensor(list(range(tp_world_size))).to(torch.device("cuda", global_rank))
    print(f"> Before split: {tensor}\n", end="")
    torch.distributed.barrier()
    split_tensor = mappings._split(tensor)
    print(f"> After split: {split_tensor}\n", end="")
    
def test_copy_to_tensor_model_parallel_region():
    print_separator(f'> Test copy_to_tensor_model_region')
    global_rank = torch.distributed.get_rank()
    # global_rank为1时，则会生成张量tensor([1])
    tensor = Parameter(torch.Tensor([global_rank]).to(torch.device("cuda", global_rank)))
    loss = global_rank * tensor
    loss.backward()
    print(f"> No copy grad: {tensor.grad}\n", end="")
    torch.distributed.barrier()
    tensor.grad = None
    # 使用copy_to_tensor_model_parallel_region对tensor进行操作
    # 该操作不会影响前向传播，仅影响反向传播
    tensor_parallel = mappings.copy_to_tensor_model_parallel_region(tensor)
    # 例：对于rank=5，则loss=5*x，其反向传播的梯度为5；依次类推
    loss_parallel = global_rank * tensor_parallel
    loss_parallel.backward()
    torch.distributed.barrier()
    print(f"> Copy grad: {tensor.grad}\n", end="")

def test_reduce_from_tensor_model_parallel_region():
    print_separator(f"> Test reduce_from_tensor_model_parallel_region")
    global_rank = torch.distributed.get_rank()
    # global_rank为1时，则会生成张量tensor([1])
    tensor1 = Parameter(torch.Tensor([global_rank]).to(torch.device("cuda", global_rank)))
    tensor2 = global_rank * tensor1
    tensor_parallel = mappings.reduce_from_tensor_model_parallel_region(tensor2)
    loss = 2 * tensor_parallel
    loss.backward()
    print(f"> loss: {loss}\n", end="")
    print(f"> grad: {tensor1.grad}\n", end="")
    
def test_scatter_to_tensor_model_parallel_region():
    print_separator(f'> Test scatter_to_tensor_model_parallel_region')
    global_rank = torch.distributed.get_rank()
    tp_world_size = mpu.get_tensor_model_parallel_world_size()
    # tensor = [1,2]
    tensor = Parameter(torch.Tensor(list(range(1, tp_world_size+1))).to(torch.device("cuda", global_rank)))
    # split之后, Rank0、Rank2、Rank4、Rank6为tensor([1]), 其余Rank为tensor([2])
    tensor_split = mappings.scatter_to_tensor_model_parallel_region(tensor)
    loss = global_rank * tensor_split
    loss.backward()
    print(f"> Before split: {tensor}\n", end="")
    torch.distributed.barrier()
    print(f"> After split: {tensor_split}\n", end="")
    torch.distributed.barrier()
    print(f"> Grad: {tensor.grad}\n", end="")

def test_gather_from_tensor_model_parallel_region():
    print_separator(f'> Test gather_from_tensor_model_parallel_region')
    global_rank = torch.distributed.get_rank()
    tensor = Parameter(torch.Tensor([global_rank]).to(torch.device("cuda", global_rank)))
    print(f"> Before gather: {tensor}\n", end="")
    torch.distributed.barrier()
    # 例: [Rank6, Rank7]的gather_tensor均为tensor([6.,7.])
    gather_tensor = mappings.gather_from_tensor_model_parallel_region(tensor)
    print(f"> After gather: {gather_tensor.data}\n", end="")
    loss = (global_rank * gather_tensor).sum()
    loss.backward()
    print(f"> Grad: {tensor.grad}\n", end="")
    
if __name__ == '__main__':
    initialize_distributed()
    world_size = torch.distributed.get_world_size()
    tensor_model_parallel_size = 2
    pipeline_model_parallel_size = 2
    # 并行环境初始化
    mpu.initialize_model_parallel(
            tensor_model_parallel_size,
            pipeline_model_parallel_size)
    test_reduce()
    test_gather()
    test_split()
    test_copy_to_tensor_model_parallel_region()
    test_reduce_from_tensor_model_parallel_region()
    test_scatter_to_tensor_model_parallel_region()
    test_gather_from_tensor_model_parallel_region()

启动脚本为文章来源地址https://www.toymoban.com/news/detail-566399.html

deepspeed test_mappings.py

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