From 883bd5d6321b5d70f2fac443864cdb835678e984 Mon Sep 17 00:00:00 2001 From: huangting4201 <1538303371@qq.com> Date: Fri, 23 Feb 2024 15:23:26 +0800 Subject: [PATCH] feat(code-docs): update doc tensor parallel (#43) --- .../locales/en/LC_MESSAGES/parallel.po | 715 +++++++++++------- doc/code-docs/source/parallel.rst | 113 ++- doc/imgs/isp.png | Bin 0 -> 303764 bytes doc/imgs/msp.png | Bin 0 -> 184833 bytes doc/imgs/mtp.png | Bin 0 -> 130640 bytes 5 files changed, 533 insertions(+), 295 deletions(-) create mode 100644 doc/imgs/isp.png create mode 100644 doc/imgs/msp.png create mode 100644 doc/imgs/mtp.png diff --git a/doc/code-docs/locales/en/LC_MESSAGES/parallel.po b/doc/code-docs/locales/en/LC_MESSAGES/parallel.po index 9ab1c5b7..326945f6 100644 --- a/doc/code-docs/locales/en/LC_MESSAGES/parallel.po +++ b/doc/code-docs/locales/en/LC_MESSAGES/parallel.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: InternLM \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-01-23 18:01+0800\n" +"POT-Creation-Date: 2024-02-04 15:50+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: en\n" @@ -27,8 +27,8 @@ msgid "" "InternEvo 支持张量并行、流水线并行、序列并行、数据并行和 ZeRO1.5 " "等并行化训练策略。在初始化分布式环境时,我们需要指定张量并行大小、流水线并行大小、数据并行大小以及 ZeRO1.5 策略。" msgstr "" -"InternEvo supports tensor parallel, pipeline parallel, sequence parallel, " -"data parallel, and ZeRO1.5 to parallelize the training pipeline. When " +"InternEvo supports tensor parallel, pipeline parallel, sequence parallel," +" data parallel, and ZeRO1.5 to parallelize the training pipeline. When " "initializing the distributed environment, we need to specify tensor " "parallel size, pipeline parallel size, data parallel size, and ZeRO1.5 " "strategy." @@ -41,8 +41,8 @@ msgid "" msgstr "" "The parallel setting of InternEvo is fully config-driven, and you can " "change the parallelism by modifying `config file " -"`_. An " -"exmaple parallel training configuration can be defined as follows:" +"`_." +" An exmaple parallel training configuration can be defined as follows:" #: ../../source/parallel.rst:19 msgid "zero1:zero 并行策略,分如下三种情况,默认值为 -1" @@ -51,297 +51,357 @@ msgstr "" "the default value is -1" #: ../../source/parallel.rst:21 -msgid "当 ``zero1 <= 0``,则 zero1 进程组的大小等于数据并行进程组的大小,因此优化器状态参数将在数据并行范围内分配" +msgid "当 ``size <= 0``,则 zero1 进程组的大小等于数据并行进程组的大小,因此优化器状态参数将在数据并行范围内分配" msgstr "" -"When ``zero1 <= 0``, the size of the zero1 process group is equal to the " +"When ``size <= 0``, the size of the zero1 process group is equal to the " "size of the data parallel process group, so the optimizer state " "parameters will be split within the data parallel range." #: ../../source/parallel.rst:22 -msgid "当 ``zero1 == 1``,则不使用 zero1 ,所有数据并行组保留完整的优化器状态参数" +msgid "当 ``size == 1``,则不使用 zero1 ,所有数据并行组保留完整的优化器状态参数" msgstr "" -"When ``zero1 == 1``, zero1 is not used, and all data parallel groups " +"When ``size == 1``, zero1 is not used, and all data parallel groups " "retain the complete optimizer state parameters." #: ../../source/parallel.rst:23 msgid "" -"当 ``zero1 > 1`` 且 ``zero1 <= data_parallel_world_size``,则 zero1 " -"进程组是数据并行进程组的子集" +"当 ``size > 1`` 且 ``size <= data_parallel_size``,则 zero1 进程组是数据并行进程组的子集" msgstr "" -"When ``zero1 > 1`` and ``zero1 <= data_parallel_world_size``, the zero1 " +"When ``size > 1`` and ``size <= data_parallel_size``, the zero1 " "process group is a subset of the data parallel process group." #: ../../source/parallel.rst:25 -msgid "tensor:张量并行大小,通常是每个节点的 GPU 数量,默认值为 1" +msgid "tensor:张量并行策略" +msgstr "tensor: tensor parallel strategy" + +#: ../../source/parallel.rst:27 +msgid "size:张量并行大小,通常是每个节点的 GPU 数量,默认值为 1" msgstr "" -"tensor: tensor parallel size, usually the number of GPUs per node, the " +"size: int, tensor parallel size, usually the number of GPUs per node, the " "default value is 1" -#: ../../source/parallel.rst:26 +#: ../../source/parallel.rst:28 +msgid "mode:张量并行模式,支持['mtp', 'msp', 'fsp', 'isp'],其中," +msgstr "mode: the tensor parallel mode, should be in ['mtp', 'msp', 'fsp', 'isp']," + +#: ../../source/parallel.rst:30 +msgid "mtp:表示使用 Megatron-LM 的张量并行实现方案,不包含序列并行,mtp 为默认模式" +msgstr "mtp: defaults to 'mtp', means the pure megatron tensor parallel without sequence parallel" + +#: ../../source/parallel.rst:31 +msgid "msp:表示使用 Megatron-LM 的序列化并行实现方案,序列并行大小 = 张量并行大小" +msgstr "msp: megatron tensor parallel with sequence parallel, sequence parallel size = tensor parallel size" + +#: ../../source/parallel.rst:32 +msgid "fsp:表示使用 flash-attn 实现模式的张量并行与序列并行,序列并行大小 = 张量并行大小" +msgstr "fsp: tensor parallel by flash-attn with sequence parallel, sequence parallel size = tensor parallel size" + +#: ../../source/parallel.rst:33 +msgid "isp:InternEvo 系统自研的序列化并行方案,可以与权重并行结合使用,序列并行大小与权重并行大小互相独立" +msgstr "isp: customed intern sequence parallel without tensor parallel, can be used with weight parallel" + +#: ../../source/parallel.rst:35 msgid "pipeline:流水线并行策略" msgstr "pipeline: pipeline parallel strategy" -#: ../../source/parallel.rst:28 +#: ../../source/parallel.rst:37 msgid "size:流水线并行大小,默认值为 1" msgstr "size: pipeline parallel size, the default value is 1" -#: ../../source/parallel.rst:29 +#: ../../source/parallel.rst:38 msgid "interleaved_overlap:bool 类型,交错式调度时,开启或关闭通信优化,默认值为 False" msgstr "" "interleaved_overlap: bool type, when interleaved scheduling, enable or " "disable communication optimization, the default value is False" -#: ../../source/parallel.rst:31 -msgid "sequence_parallel:是否开启序列化并行,默认值为 False" -msgstr "" -"sequence_parallel: whether to enable sequence parallelism, the default " -"value is False" +#: ../../source/parallel.rst:40 +msgid "weight:权重并行策略,只能与 isp 张量并行模式结合使用" +msgstr "weight: weight parallel strategy, only can be used with 'isp' tensor parallel mode" -#: ../../source/parallel.rst:33 +#: ../../source/parallel.rst:42 +msgid "size:权重并行大小,默认值为 1" +msgstr "size: weight parallel size, the default value is 1" + +#: ../../source/parallel.rst:43 +msgid "overlap:是否开启计算与通信的 overlap,默认值为 False" +msgstr "overlap: bool, enable/disable all_gather/reduce_scatter communication overlap, defaults to False" + +#: ../../source/parallel.rst:44 +msgid "memory_pool:是否开启权重显存池,默认值为 False" +msgstr "memory_pool: bool, enable/disable memory pool, defaults to False" + +#: ../../source/parallel.rst:46 msgid "注意:数据并行大小 = 总的 GPU 数目 / 流水线并行大小 / 张量并行大小" msgstr "" "Note: `Data parallel size = Total number of GPUs / Pipeline parallel size" " / Tensor parallel size`" -#: ../../source/parallel.rst:36 +#: ../../source/parallel.rst:49 msgid "张量并行" msgstr "Tensor Parallel" -#: ../../source/parallel.rst:38 +#: ../../source/parallel.rst:51 msgid "" -"InternEvo 的张量并行实现方案基于 `flash attention `_, 主要对 `attention " -"`_" -" 和 `linear " -"`_" -" 这两个模块进行张量并行操作。" +"InternEvo 系统 ``v0.3.0`` 版本在张量并行策略上有较大更新,目前的张量并行支持四种模式配置['mtp', 'msp', " +"'fsp', 'isp'],前三种模式为基于 Megatron-LM 的张量并行和序列化并行的策略实现,最后一种模式为 InternEvo " +"系统自研,可与权重并行(Weight Parallel)结合使用的一种新的序列化并行方式。接下来详细介绍这几种张量并行模式的区别。" msgstr "" -"The implementation of tensor parallel for InternEvo is based on `flash " -"attention `_, which has " -"tensor parallel extensions to parallelize `attention " -"`_" -" and `linear " -"`_" -" blocks in InternEvo. " - -#: ../../source/parallel.rst:41 -msgid "用户可通过配置文件中的 ``parallel.tensor`` 字段来设置张量并行大小。" -msgstr "" -"To use tensor parallel, you need to set the value of tensor parallel size" -" ``parallel.tensor`` in the config file, which is usually the number of " -"GPUs per node." +"The InternEvo system version ``v0.3.0`` has significant updates in the tensor parallelism strategy. " +"The current tensor parallelism supports four modes: ['mtp', 'msp', 'fsp', 'isp']. The first three modes " +"are based on the Megatron-LM's tensor parallelism and sequence parallelism strategy. The last mode is a " +"self-developed strategy by the InternEvo system, which is a new sequence parallelism method that can be used " +"in conjunction with weight parallelism. The following provides a detailed explanation of the differences among " +"these tensor parallelism modes." -#: ../../source/parallel.rst:47 -msgid "张量并行,采用自 `flash-attention `_" +#: ../../source/parallel.rst:53 +msgid "MTP" msgstr "" -"Tensor parallel, adopted from `flash-attention " -"`_" -#: ../../source/parallel.rst:50 -msgid "流水线并行" -msgstr "Pipeline Parallel" - -#: ../../source/parallel.rst:52 +#: ../../source/parallel.rst:55 msgid "" -"InternEvo 在流水线并行中使用 `1F1B `_ " -"(1F1B,一次前向传递后跟一次反向传递)策略。对于 1F1B 策略,有两种实现方式:" +"MTP(Megatron-LM Tensor Parallel), 为默认的张量并行模型,引用自 `Megatron-LM Tensor " +"Parallel `_ 并行方案,如下图所示,带有张量并行的 " +"``Transformer`` 层:" msgstr "" -"InternEvo uses `1F1B `_ (one forward" -" pass followed by one backward pass) for pipeline parallel. For 1F1B " -"strategy, there are two implementations:" +"MTP (Megatron-LM Tensor Parallel) is the default tensor parallelism model, inspired by the Megatron-LM Tensor Parallel parallel scheme, " +"as referenced in the paper `Megatron-LM Tensor Parallel `_. " +"The following diagram illustrates the ``Transformer`` layer with tensor parallelism:" -#: ../../source/parallel.rst:54 -msgid "非交错调度器,内存高效。" -msgstr "non-interleaved scheduler, which is memory-efficient" - -#: ../../source/parallel.rst:55 -msgid "交错调度器,内存高效且时间高效(GPU空泡较少)。" -msgstr "interleaved scheduler, which is both memory-efficient and time-efficient." #: ../../source/parallel.rst:61 -msgid "1F1B 流水线并行调度器,采用自 `Megatron-LM `_" +msgid "Transformer layer with tensor parallelism." msgstr "" -"Non-interleaved and interleaved scheduler for 1F1B pipeline parallelism, " -"adopted from `Megatron-LM `_" -#: ../../source/parallel.rst:64 -msgid "非交错式流水线调度" -msgstr "scheduler for non-interleaved 1F1B strategy" - -#: ../../source/parallel.rst:65 -msgid "如果要使用非交错式调度, 需要设置 ``model.num_chunks = 1``。" +#: ../../source/parallel.rst:63 +msgid "" +"MTP 主要对 `attention " +"`_" +" 和 `linear " +"`_" +" 这两个模块进行张量并行操作。假设张量并行大小为 ``tp``,输入数据的序列长度为 ``seqlen``,隐藏层大小为 ``hidden " +"size``,则张量并行期间产生的激活值 ``shape`` 为 ``[seqlen, hidden_size/tp]``。" msgstr "" -"To use non-interleaved pipeline scheduler, users need to set " -"``model.num_chunks = 1`` in the config file." +"MTP primarily applies tensor parallelism operations to the attention and the linear module. " +"Assuming the tensor parallelism size is tp, the sequence length of input data is seqlen, and the hidden layer size is hidden size, " +"then the shape of the activation values generated during tensor parallelism is ``[seqlen, hidden_size/tp]``." -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:1 of +#: ../../source/parallel.rst:65 msgid "" -"A helper schedule class for pipeline parallelism running environment. It " -"uses non-interleaved 1F1B strategy. Other properties are similar as " -":class:`NonPipelineSchedule`." +"MTP 张量并行引入的通信如上图所示,其中 ``f`` 和 ``f̄`` 是共轭的。在前向传递中 ``f`` 是无操作,而在反向传递中进行 " +"``all-reduce`` 操作。而 ``f̄`` 在前向传递中进行 ``all-reduce`` 操作,在反向传递中是无操作。" msgstr "" +"The communication introduced by MTP is illustrated in the above diagram, where ``f`` and ``f̄`` are conjugates. " +"In the forward pass, ``f`` corresponds to a no-operation, while in the backward pass, it involves an ``all-reduce`` operation. " +"On the other hand, ``f̄`` performs an ``all-reduce`` operation in the forward pass and is a no-operation in the backward pass." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.pre_processing -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.belongs_to_current_rank -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.step -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.zero_grad of -msgid "参数" +#: ../../source/parallel.rst:67 +msgid "MSP" msgstr "" -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:5 of -msgid "The number of microbatches." +#: ../../source/parallel.rst:69 +msgid "" +"MSP(Megatron-LM Sequence Parallel),引用自 `Megatron-LM Sequence Parallel " +"`_ 并行方案,如下图所示,带有张量并行和序列化并行的 " +"``Transformer`` 层:" msgstr "" +"MSP (Megatron-LM Sequence Parallel) is adopted from the Megatron-LM Sequence Parallel parallel scheme. " +"The following diagram illustrates the Transformer layer with both tensor parallelism and sequence parallelism:" -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:7 of -msgid "Type of data. torch.float by default." +#: ../../source/parallel.rst:75 +msgid "Transformer layer with tensor and sequence parallelism." msgstr "" -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:9 of +#: ../../source/parallel.rst:77 msgid "" -"The post processing function which receives a micro batch of data, and it" -" will be executed in `load_micro_batch`." -msgstr "" - -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:12 of -msgid "Specified shape in pipeline communication." +"与 MTP 对比,我们可以发现,MSP 主要针对未进行张量并行的模块,如 ``LayerNorm`` 和 ``Dropout`` " +"等模型进行序列化并行操作。需要注意的是,序列化并行大小与张量并行大小相等,且共用通信组。假设张量并行大小为 ``tp``,输入数据的序列长度为 " +"``seqlen``,隐藏层大小为 ``hidden size``,则序列化并行期间产生的激活值形状为 ``[seqlen/tp, " +"hidden_size]``,张量并行期间产生的激活值形状为 ``[seqlen, hidden_size/tp]``。" msgstr "" +"Compared to MTP, it is evident that MSP primarily focuses on modules without tensor parallelism, such as ``LayerNorm`` " +"and ``Dropout``, and performs sequence parallelism operations. It is important to note that the size of sequence parallelism " +"is equal to the size of tensor parallelism, and they share the same communication group. Assuming the tensor parallelism size is tp, " +"the input data has a sequence length of seqlen, and the hidden layer size is hidden size, the shape of activation values during " +"sequence parallelism is ``[seqlen/tp, hidden_size]``, while during tensor parallelism, it is ``[seqlen, hidden_size/tp]``." -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:14 of +#: ../../source/parallel.rst:79 msgid "" -"If set to `True`, communication will be reduced over pipeline when using " -"1D tensor parallelization." +"MSP与MTP相比,通信原语有所变化,如上图所示 ``g`` 和 ``ḡ`` 是共轭的。在前向传递中 ``g`` 进行 ``all-" +"gather`` 操作,而在反向传递中进行 ``reduce-scatter`` 操作。而 ``ḡ`` 在前向传递中进行 ``reduce-" +"scatter`` 操作,在反向传递中进行 ``all-gather`` 操作。" msgstr "" +"In comparison to MTP, there are variations in the communication primitives in MSP, as illustrated in the diagram above, " +"where ``g`` and ``ḡ`` are conjugates. In the forward pass, ``g`` performs an ``all-gather`` operation, while in the backward pass, " +"it undergoes a ``reduce-scatter`` operation. On the other hand, ``ḡ`` conducts a ``reduce-scatter`` operation in the forward pass and " +"an ``all-gather`` operation in the backward pass." -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler:16 of -msgid "List of scheduler hooks." +#: ../../source/parallel.rst:81 +msgid "" +"在前向传递中 ``g`` 通信处于序列化并行和张量并行的交接处,进行的是激活值在 ``seqlen`` 维度的 ``all-gather`` " +"操作,该通信完成后,激活值形状变成完整的 ``[seqlen, hidden_size]``,然后进入张量并行模块范围。``ḡ`` " +"通信处于张量并行和序列化并行的交接处,需要把 MTP 中的 ``all-reduce`` 通信操作变成 ``reduce-" +"scatter``,才能完成 ``seqlen`` 维度的切分,激活值形状变成 ``[seqlen/tp, " +"hidden_size]``,从而正常进入序列化并行的阶段。而反向传递时,则是同样的道理。" msgstr "" +"In the forward pass, the communication of ``g`` occurs at the junction of sequence parallelism and tensor parallelism, " +"performing an ``all-gather`` operation along the seqlen dimension of activation values. After this communication is completed, " +"the shape of the activation values becomes the full ``[seqlen, hidden_size]``, and then it enters the scope of the tensor parallelism module. " +"The communication of ``ḡ`` is situated at the junction of tensor parallelism and sequence parallelism, requiring the transformation of the " +"``all-reduce`` communication operation from MTP into a ``reduce-scatter`` operation to achieve the split along the seqlen dimension. " +"This results in the activation values having a shape of ``[seqlen/tp, hidden_size]``, enabling a smooth transition into the sequence parallelism phase. " +"The same principles apply during the backward pass." -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.pre_processing:1 -#: of -msgid "To perform actions before running the schedule." +#: ../../source/parallel.rst:83 +msgid "FSP" msgstr "" -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.pre_processing:3 -#: of -msgid "InternLM engine for training and inference." +#: ../../source/parallel.rst:85 +msgid "" +"FSP(Flash-Attn Sequence Parallel),引用自 `flash attention " +"`_ 的序列化并行实现方案。该实现方案与 MSP " +"的唯一区别在于,在 ``g`` 进行 ``all-gather`` 通信后,MSP 会存储一份完整的输入数据用于 backward 计算,而 " +"FSP 则只存储 ``seqlen`` 切分后的输入数据,因此在进行 backward 计算时,需要再额外 ``all-gather`` " +"一次输入。" msgstr "" +"FSP (Flash-Attn Sequence Parallel) is a sequence parallelism implementation inspired by the flash attention scheme, as referenced in " +"`flash attention `_. The only difference between this implementation and MSP is that, " +"after the ``g`` performs ``all-gather`` communication, MSP stores a complete copy of the input data for backward computation, while FSP only " +"retains the input data split into seqlen segments. Therefore, during backward computation, an additional ``all-gather`` operation " +"is needed to retrieve the complete input data." -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:1 -#: of -msgid "" -"Runs non-interleaved 1F1B schedule, with communication between pipeline " -"stages. Returns a tuple with losses if the last stage, an empty tuple " -"otherwise." +#: ../../source/parallel.rst:87 +msgid "因此,FSP 与 MSP 性能对比的话,会有更小的显存占用,但是由于引入额外的 ``all-gather`` 通信,会导致训练速度 TGS 降低。" msgstr "" +"Therefore, in terms of performance comparison between FSP and MSP, FSP tends to have a smaller memory footprint. However, " +"the introduction of additional ``all-gather`` communication can lead to a reduction in the training speed, denoted as TGS." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:4 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:4 -#: of -msgid "Colossalai engine for training and inference." +#: ../../source/parallel.rst:90 +msgid "ISP" msgstr "" -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:6 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:6 -#: of +#: ../../source/parallel.rst:92 msgid "" -"Dataloader as the form of an iterator, obtained by calling " -"iter(dataloader)." +"ISP(Intern Sequence Parallel),InternEvo " +"系统自研的灵活可扩展序列化并行方案,支持张量并行与序列化并行解耦,通过计算和通信的overlap提高训练性能,并基于显存池管理降低显存碎片化的可能性,提高显存利用率。" msgstr "" +"ISP (Intern Sequence Parallel) is a flexible and scalable sequence parallelism solution developed in-house by the InternEvo system. " +"It supports the decoupling of tensor parallelism and sequence parallelism, enhancing training performance through the overlap of computation and communication. " +"Additionally, it incorporates memory pool management to reduce the likelihood of memory fragmentation, thereby improving memory utilization." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:8 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:8 -#: of +#: ../../source/parallel.rst:94 +msgid "" +"以 `configs/7B_isp_sft.py " +"`_" +" 配置文件为例,将 ``tensor.mode`` 字段设置为 ``isp``,而 ``tensor.size`` 字段代表的是数据 " +"``seqlen`` 维度切分大小。ISP 算法可与 ``weight parallel`` 结合使用,其中 ``weight.size`` " +"字段代表的是模型权重切分大小,将 ``weight.overlap`` 字段设置为 ``True`` 即为开启计算与通信的 " +"``overlap``,可提高训练性能。将 ``weight.memory_pool`` 字段设置为 ``True`` " +"即为开启显存池管理功能,可一定程度降低 GPU 显存碎片的可能性,提高显存利用率。" +msgstr "" +"Taking the configuration file `configs/7B_isp_sft.py `_ as an example, " +"set the tensor.mode field to isp, where the tensor.size field represents the size of data split along the seqlen dimension. " +"The ISP algorithm can be combined with weight parallel, where the weight.size field represents the model weight split size. " +"Setting weight.overlap to True enables computation and communication overlap, enhancing training performance. " +"Setting weight.memory_pool to True activates the memory pool management feature, which helps to some extent " +"in reducing the likelihood of GPU memory fragmentation and improving memory utilization." + +#: ../../source/parallel.rst:105 +msgid "如下图所示,带有序列化并行和权重并行的 ``Transformer`` 层:" +msgstr "As illustrated in the diagram below, there is a Transformer layer with both sequence parallelism and weight parallelism:" + +#: ../../source/parallel.rst:111 msgid "" -"Whether run forward step only. Default is false. If true, no backward " -"will be run." +"如图所示,ISP 的序列化并行范围覆盖整个 ``Transformer`` 模型层,模型权重并行主要针对 ``Attention`` 和 " +"``MLP Block`` 的 ``Linear module``。" msgstr "" +"As shown in the figure, the sequence parallelism scope of ISP covers the entire Transformer model layer, " +"while the weight parallelism primarily targets the Linear module within the Attention and MLP Block." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:10 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:10 -#: of -msgid "Whether returns the loss value. Default is true." +#: ../../source/parallel.rst:113 +msgid "" +"通信原语变化情况为,在前向传递时,每个 ``Linear`` 需要进行模型权重的 ``all-gather`` 通信;在后向传递时,每个 " +"``Linear`` 在进行后向计算前需要进行模型权重的 ``all-gather`` 通信,在后向计算后,需要进行模型权重的梯度的 " +"``reduce-scatter`` 通信操作。" msgstr "" +"The changes in communication primitives are as follows: during the forward pass, each Linear module requires ``all-gather`` communication " +"for model weight. In the backward pass, before performing the backward computation, each Linear module requires ``all-gather`` communication for model weight. " +"After the backward computation, there is a ``reduce-scatter`` communication operation for the gradients of model weights." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:12 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:12 -#: of -msgid "If False, the output and label won't be returned." -msgstr "" -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.belongs_to_current_rank -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.step of -msgid "返回" +#: ../../source/parallel.rst:115 +msgid "" +"需要注意的是,与 MSP 和 FSP 相比,在进行 ``attention score`` 计算时,ISP 也有通信原语的一些变化,如 " +"``Self-Atten`` 前后各增加了一个 ``all-to-all`` 通信操作,用于完成激活值形状的转置,目的是在进行 " +"``attention score`` 计算时能保持原有的张量并行的模式。" msgstr "" +"It is important to note that, in comparison to MSP and FSP, there are some changes in communication primitives for attention score calculation in ISP. " +"For instance, before and after ``Self-Atten``, an additional ``all-to-all`` communication operation is introduced to transpose the shape of activation values. " +"The purpose is to maintain the original tensor parallelism pattern during the attention score calculation." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:15 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:15 -#: of +#: ../../source/parallel.rst:117 msgid "" -"A tuple of (output, label, loss, moe_loss), loss and label could be None." -" The loss would be returned only in the last stage. And the moe_loss " -"is accumulated from all stages." +"关于 ISP 算法更多的设计思路和性能评测,请参考论文 `InternEvo: Efficient Long-sequence Large " +"Language Model Training via Hybrid Parallelism and Redundant Sharding " +"`_。" msgstr "" +"For more design details and performance evaluation of the ISP algorithm, please refer to the paper " +"`InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding " +"`_." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:17 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:17 -#: of -msgid "A tuple of (output, label, loss, moe_loss), loss and label could be None." -msgstr "" +#: ../../source/parallel.rst:121 +msgid "流水线并行" +msgstr "Pipeline Parallel" -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:18 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:18 -#: of +#: ../../source/parallel.rst:123 msgid "" -"The loss would be returned only in the last stage. And the moe_loss is " -"accumulated from all stages." +"InternEvo 在流水线并行中使用 `1F1B `_ " +"(1F1B,一次前向传递后跟一次反向传递)策略。对于 1F1B 策略,有两种实现方式:" msgstr "" +"InternEvo uses `1F1B `_ (one " +"forward pass followed by one backward pass) for pipeline parallel. For " +"1F1B strategy, there are two implementations:" -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.belongs_to_current_rank -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.step of -msgid "返回类型" +#: ../../source/parallel.rst:125 +msgid "非交错调度器,内存高效。" +msgstr "non-interleaved scheduler, which is memory-efficient" + +#: ../../source/parallel.rst:126 +msgid "交错调度器,内存高效且时间高效(GPU空泡较少)。" +msgstr "interleaved scheduler, which is both memory-efficient and time-efficient." + +#: ../../source/parallel.rst:132 +msgid "1F1B 流水线并行调度器,采用自 `Megatron-LM `_" msgstr "" +"Non-interleaved and interleaved scheduler for 1F1B pipeline parallelism, " +"adopted from `Megatron-LM `_" -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:19 -#: internlm.core.scheduler.pipeline_scheduler.PipelineScheduler.forward_backward_step:19 -#: of -msgid "Tuple[:class:`torch.Tensor`]" +#: ../../source/parallel.rst:135 +msgid "非交错式流水线调度" +msgstr "scheduler for non-interleaved 1F1B strategy" + +#: ../../source/parallel.rst:136 +msgid "如果要使用非交错式调度, 需要设置 ``model.num_chunks = 1``。" msgstr "" +"To use non-interleaved pipeline scheduler, users need to set " +"``model.num_chunks = 1`` in the config file." -#: ../../source/parallel.rst:71 +#: ../../source/parallel.rst:142 msgid "交错式流水线调度" msgstr "scheduler for interleaved 1F1B strategy" -#: ../../source/parallel.rst:72 +#: ../../source/parallel.rst:143 msgid "如果要使用交错式调度, 需要设置 ``model.num_chunks > 1``。" msgstr "" "To use interleaved pipeline scheduler, users need to set " "``model.num_chunks > 1`` in the config file." -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler:1 of -msgid "Interleaved Pipeline Scheduler." -msgstr "" - -#: internlm.core.scheduler.pipeline_scheduler.InterleavedPipelineScheduler.forward_backward_step:1 -#: of -msgid "" -"Run interleaved 1F1B schedule (model split into model chunks), with " -"communication between pipeline stages as needed." -msgstr "" - -#: ../../source/parallel.rst:77 +#: ../../source/parallel.rst:148 msgid "值得注意的是,在使用交错式流水线调度器时可启用通信优化功能,即在 1F1B 阶段启用异步通信,以充分利用上行/下行带宽并实现通信与计算重叠。" msgstr "" "Asynchronous communication will be enabled in 1F1B stage to make full use" " of uplink/downlink bandwidth and achieve communication overlap. " -#: ../../source/parallel.rst:79 +#: ../../source/parallel.rst:150 msgid "" "用户需要在配置文件中设置 ``parallel.pipeline.interleaved_overlap = " "True``。该功能启用后,将调用函数 " @@ -353,56 +413,31 @@ msgstr "" "called and ``internlm.core.communication.AsynCommunicator`` will be " "created for managing async communication." -#: ../../source/parallel.rst:81 +#: ../../source/parallel.rst:152 msgid "``1F1B-without-overlap`` 和 ``1F1B-with-overlap`` 的区别如下所示:" msgstr "" "The difference between 1F1B stage without overlap and 1F1B stage with " "overlap is shown as follows:" -#: ../../source/parallel.rst:102 -msgid "序列并行" -msgstr "Sequence Parallel" - -#: ../../source/parallel.rst:104 -msgid "" -"序列并行是一种在不引入额外计算、通信和内存开销的情况下,减少层 ``layer_norm`` 和 ``dropout`` " -"操作中的激活值内存。InternEvo 中的序列并行实现基于 `flash attention `_。这个并行策略有助于降低模型的内存消耗,提高了模型在资源受限环境中的可扩展性。" -msgstr "" -"Sequence parallel is a technique to reduce activation memory in layer " -"norm and dropout without additional computation, communication or memory " -"overhead. The implementation of sequence parallel for InternEvo is based " -"on `flash attention `_. " - -#: ../../source/parallel.rst:106 -msgid "如果要启用序列并行, 用户需要设置 ``parallel.sequence_parallel = True``。" -msgstr "" -"To enable sequence parallel, you need to set ``parallel.sequence_parallel" -" = True`` in the config file." - -#: ../../source/parallel.rst:112 -msgid "序列并行, 采用自 flash-attention" -msgstr "Sequence parallel, adopted from flash-attention" - -#: ../../source/parallel.rst:115 +#: ../../source/parallel.rst:172 msgid "数据并行" msgstr "Data Parallel" -#: ../../source/parallel.rst:117 +#: ../../source/parallel.rst:174 msgid "InternEvo 支持数据并行。数据并行大小为:" msgstr "InternEvo supports data parallel. For data parallel:" -#: ../../source/parallel.rst:119 +#: ../../source/parallel.rst:176 msgid "" "`Data parallel size = Total number of GPUs / Pipeline parallel size / " "Tensor parallel size`" msgstr "" -#: ../../source/parallel.rst:122 +#: ../../source/parallel.rst:179 msgid "ZeRO1.5" msgstr "" -#: ../../source/parallel.rst:124 +#: ../../source/parallel.rst:181 msgid "" "ZeRO1.5 的实现使用了分层分片的概念,通过配置值 ``parallel.zero1`` " "启用了本地节点内的分片。这个方法有助于有效管理和分配模型参数和梯度,以减少内存使用并提高训练效率。" @@ -411,20 +446,20 @@ msgstr "" "via config value ``parallel.zero1``, which enables sharding within local " "nodes." -#: ../../source/parallel.rst:126 +#: ../../source/parallel.rst:183 msgid "当 ``parallel.zero1 <= 0``,则 zero1 进程组的大小等于数据并行进程组的大小,因此优化器状态参数将在数据并行范围内分配" msgstr "" "If ``parallel.zero1 <= 0``, the size of the zero process group is equal " "to the size of the dp process group, so parameters will be divided within" " the range of dp." -#: ../../source/parallel.rst:127 +#: ../../source/parallel.rst:184 msgid "当 ``parallel.zero1 == 1``,则不使用 zero1 ,所有数据并行组保留完整的优化器状态参数" msgstr "" "If ``parallel.zero1 == 1``, zero is not used, and all dp groups retain " "the full amount of model parameters." -#: ../../source/parallel.rst:128 +#: ../../source/parallel.rst:185 msgid "" "当 ``parallel.zero1 > 1`` 且 ``parallel.zero1 <= " "data_parallel_world_size``,则 zero1 进程组是数据并行进程组的子集" @@ -434,7 +469,7 @@ msgstr "" "is usually a better choice to split the parameters within nodes with a " "setting ``parallel.zero1 <= 8``." -#: ../../source/parallel.rst:130 +#: ../../source/parallel.rst:187 msgid "" "此外,用户可以在配置文件中通过 ``hybrid_zero_optimizer`` " "字段启用优化器的通信优化功能,设置桶大小,以及梯度剪裁等参数。这些设置有助于优化训练过程中的通信和计算效率,以及梯度的处理方式。" @@ -442,11 +477,11 @@ msgstr "" "Furthermore, you can enable communication-computation overlap, set bucket" " reduce size, gradient clipping parameters in the config file." -#: ../../source/parallel.rst:144 +#: ../../source/parallel.rst:201 msgid "这里有两个值得关注的通信优化点:" msgstr "There are two communication optimizations worth paying attention to here:" -#: ../../source/parallel.rst:146 +#: ../../source/parallel.rst:203 msgid "" "overlap_sync_grad: 如果设置为 ``True``,则将训练的 ``backward pass`` 与梯度的 ``all-" "reduce`` 通信重叠" @@ -454,7 +489,7 @@ msgstr "" "overlap_sync_grad: If set True, overlapping training backward pass with " "gradients' all-reduce communication." -#: ../../source/parallel.rst:147 +#: ../../source/parallel.rst:204 msgid "" "overlap_sync_param: 如果设置为 ``True``,则将参数的 ``broadcast`` 通信与下一步的 ``forward " "pass`` 进行重叠" @@ -462,69 +497,215 @@ msgstr "" "overlap_sync_param: If set True, overlapping parameters' broadcast " "communication with next step's forward pass." -#: ../../source/parallel.rst:149 +#: ../../source/parallel.rst:206 msgid "这些优化可以加速训练过程,提高训练效率。" msgstr "" "These optimizations can speed up the training process and improve " "training efficiency." -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer:1 of -msgid "Hybrid Zero Optimizer." -msgstr "" +#~ msgid "A tuple of (output, label, loss), loss and label could be None." +#~ msgstr "" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.belongs_to_current_rank:1 -#: of -msgid "" -"Check whether a parameter is supposed to be updated by the process of the" -" current rank" -msgstr "" +#~ msgid "" +#~ "A tuple of (output, label, loss), " +#~ "loss and label could be None. " +#~ "The loss would be returned only in" +#~ " the last stage." +#~ msgstr "" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.belongs_to_current_rank:3 -#: of -msgid "A :class:`torch.Tensor` object" -msgstr "" +#~ msgid "The loss would be returned only in the last stage." +#~ msgstr "" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.belongs_to_current_rank:6 -#: of -msgid "" -"True if the parameter should be updated by the current rank. Otherwise " -"false." -msgstr "" +#~ msgid "sequence_parallel:是否开启序列化并行,默认值为 False" +#~ msgstr "" +#~ "sequence_parallel: whether to enable sequence" +#~ " parallelism, the default value is " +#~ "False" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.zero_grad:1 -#: of -msgid "" -"Set parameter gradients to zero. If set_to_none = True, gradient will be " -"set to None to save memory." -msgstr "" +#~ msgid "用户可通过配置文件中的 ``parallel.tensor`` 字段来设置张量并行大小。" +#~ msgstr "" +#~ "To use tensor parallel, you need " +#~ "to set the value of tensor " +#~ "parallel size ``parallel.tensor`` in the " +#~ "config file, which is usually the " +#~ "number of GPUs per node." -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.zero_grad:4 -#: of -msgid "Whether set the gradient to None. Default value is True." -msgstr "" +#~ msgid "张量并行,采用自 `flash-attention `_" +#~ msgstr "" +#~ "Tensor parallel, adopted from `flash-" +#~ "attention `_" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.step:1 of -msgid "Performs a single optimization step." -msgstr "" +#~ msgid "" +#~ "A helper schedule class for pipeline " +#~ "parallelism running environment. It uses " +#~ "non-interleaved 1F1B strategy. Other " +#~ "properties are similar as " +#~ ":class:`NonPipelineSchedule`." +#~ msgstr "" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.step:3 of -msgid "A closure that reevaluates the model and returns the loss." -msgstr "" +#~ msgid "参数" +#~ msgstr "" -#: internlm.solver.optimizer.hybrid_zero_optim.HybridZeroOptimizer.step:7 of -msgid "Whether the gradient is success updated, and the gradient." -msgstr "" +#~ msgid "The number of microbatches." +#~ msgstr "" -#~ msgid "A tuple of (output, label, loss), loss and label could be None." +#~ msgid "Type of data. torch.float by default." +#~ msgstr "" + +#~ msgid "" +#~ "The post processing function which " +#~ "receives a micro batch of data, " +#~ "and it will be executed in " +#~ "`load_micro_batch`." +#~ msgstr "" + +#~ msgid "Specified shape in pipeline communication." +#~ msgstr "" + +#~ msgid "" +#~ "If set to `True`, communication will " +#~ "be reduced over pipeline when using " +#~ "1D tensor parallelization." +#~ msgstr "" + +#~ msgid "List of scheduler hooks." +#~ msgstr "" + +#~ msgid "To perform actions before running the schedule." +#~ msgstr "" + +#~ msgid "InternLM engine for training and inference." +#~ msgstr "" + +#~ msgid "" +#~ "Runs non-interleaved 1F1B schedule, with" +#~ " communication between pipeline stages. " +#~ "Returns a tuple with losses if the" +#~ " last stage, an empty tuple " +#~ "otherwise." +#~ msgstr "" + +#~ msgid "Colossalai engine for training and inference." +#~ msgstr "" + +#~ msgid "" +#~ "Dataloader as the form of an " +#~ "iterator, obtained by calling " +#~ "iter(dataloader)." +#~ msgstr "" + +#~ msgid "" +#~ "Whether run forward step only. Default" +#~ " is false. If true, no backward " +#~ "will be run." +#~ msgstr "" + +#~ msgid "Whether returns the loss value. Default is true." +#~ msgstr "" + +#~ msgid "If False, the output and label won't be returned." +#~ msgstr "" + +#~ msgid "返回" +#~ msgstr "" + +#~ msgid "" +#~ "A tuple of (output, label, loss, " +#~ "moe_loss), loss and label could be " +#~ "None. The loss would be returned " +#~ "only in the last stage. And the" +#~ " moe_loss is accumulated from all " +#~ "stages." +#~ msgstr "" + +#~ msgid "" +#~ "A tuple of (output, label, loss, " +#~ "moe_loss), loss and label could be " +#~ "None." #~ msgstr "" #~ msgid "" -#~ "A tuple of (output, label, loss), " -#~ "loss and label could be None. " #~ "The loss would be returned only in" -#~ " the last stage." +#~ " the last stage. And the moe_loss " +#~ "is accumulated from all stages." #~ msgstr "" -#~ msgid "The loss would be returned only in the last stage." +#~ msgid "返回类型" +#~ msgstr "" + +#~ msgid "Tuple[:class:`torch.Tensor`]" +#~ msgstr "" + +#~ msgid "Interleaved Pipeline Scheduler." +#~ msgstr "" + +#~ msgid "" +#~ "Run interleaved 1F1B schedule (model " +#~ "split into model chunks), with " +#~ "communication between pipeline stages as " +#~ "needed." +#~ msgstr "" + +#~ msgid "序列并行" +#~ msgstr "Sequence Parallel" + +#~ msgid "" +#~ "序列并行是一种在不引入额外计算、通信和内存开销的情况下,减少层 ``layer_norm`` 和 " +#~ "``dropout`` 操作中的激活值内存。InternEvo 中的序列并行实现基于 `flash" +#~ " attention `_。这个并行策略有助于降低模型的内存消耗,提高了模型在资源受限环境中的可扩展性。" +#~ msgstr "" +#~ "Sequence parallel is a technique to " +#~ "reduce activation memory in layer norm" +#~ " and dropout without additional " +#~ "computation, communication or memory overhead." +#~ " The implementation of sequence parallel" +#~ " for InternEvo is based on `flash " +#~ "attention `_. " + +#~ msgid "如果要启用序列并行, 用户需要设置 ``parallel.sequence_parallel = True``。" +#~ msgstr "" +#~ "To enable sequence parallel, you need" +#~ " to set ``parallel.sequence_parallel = " +#~ "True`` in the config file." + +#~ msgid "序列并行, 采用自 flash-attention" +#~ msgstr "Sequence parallel, adopted from flash-attention" + +#~ msgid "Hybrid Zero Optimizer." +#~ msgstr "" + +#~ msgid "" +#~ "Check whether a parameter is supposed" +#~ " to be updated by the process " +#~ "of the current rank" +#~ msgstr "" + +#~ msgid "A :class:`torch.Tensor` object" +#~ msgstr "" + +#~ msgid "" +#~ "True if the parameter should be " +#~ "updated by the current rank. Otherwise" +#~ " false." +#~ msgstr "" + +#~ msgid "" +#~ "Set parameter gradients to zero. If " +#~ "set_to_none = True, gradient will be " +#~ "set to None to save memory." +#~ msgstr "" + +#~ msgid "Whether set the gradient to None. Default value is True." +#~ msgstr "" + +#~ msgid "Performs a single optimization step." +#~ msgstr "" + +#~ msgid "A closure that reevaluates the model and returns the loss." +#~ msgstr "" + +#~ msgid "Whether the gradient is success updated, and the gradient." #~ msgstr "" diff --git a/doc/code-docs/source/parallel.rst b/doc/code-docs/source/parallel.rst index 211efe86..496b7be6 100644 --- a/doc/code-docs/source/parallel.rst +++ b/doc/code-docs/source/parallel.rst @@ -10,41 +10,112 @@ InternEvo 的并行设置由配置文件中的 ``parallel`` 字段指定,用 .. code-block:: python parallel = dict( - zero1=8, - tensor=1, + zero1=dict(size=8), + tensor=dict(size=1, mode="mtp"), pipeline=dict(size=1, interleaved_overlap=True), - sequence_parallel=False, + weight=dict(size=1, overlap=True, memory_pool=True), ) - zero1:zero 并行策略,分如下三种情况,默认值为 -1 - - 当 ``zero1 <= 0``,则 zero1 进程组的大小等于数据并行进程组的大小,因此优化器状态参数将在数据并行范围内分配 - - 当 ``zero1 == 1``,则不使用 zero1 ,所有数据并行组保留完整的优化器状态参数 - - 当 ``zero1 > 1`` 且 ``zero1 <= data_parallel_world_size``,则 zero1 进程组是数据并行进程组的子集 + - 当 ``size <= 0``,则 zero1 进程组的大小等于数据并行进程组的大小,因此优化器状态参数将在数据并行范围内分配 + - 当 ``size == 1``,则不使用 zero1 ,所有数据并行组保留完整的优化器状态参数 + - 当 ``size > 1`` 且 ``zero1 <= data_parallel_size``,则 zero1 进程组是数据并行进程组的子集 + +- tensor:张量并行策略 + + - size:张量并行大小,通常是每个节点的 GPU 数量,默认值为 1 + - mode:张量并行模式,支持['mtp', 'msp', 'fsp', 'isp'],其中, + + - mtp:表示使用 Megatron-LM 的张量并行实现方案,不包含序列并行,mtp 为默认模式 + - msp:表示使用 Megatron-LM 的序列化并行实现方案,序列并行大小 = 张量并行大小 + - fsp:表示使用 flash-attn 实现模式的张量并行与序列并行,序列并行大小 = 张量并行大小 + - isp:InternEvo 系统自研的序列化并行方案,可以与权重并行结合使用,序列并行大小与权重并行大小互相独立 -- tensor:张量并行大小,通常是每个节点的 GPU 数量,默认值为 1 - pipeline:流水线并行策略 - size:流水线并行大小,默认值为 1 - interleaved_overlap:bool 类型,交错式调度时,开启或关闭通信优化,默认值为 False -- sequence_parallel:是否开启序列化并行,默认值为 False +- weight:权重并行策略,只能与 isp 张量并行模式结合使用 + + - size:权重并行大小,默认值为 1 + - overlap:是否开启计算与通信的 overlap,默认值为 False + - memory_pool:是否开启权重显存池,默认值为 False 注意:数据并行大小 = 总的 GPU 数目 / 流水线并行大小 / 张量并行大小 张量并行 ----------------- -InternEvo 的张量并行实现方案基于 `flash attention `_, 主要对 `attention `_ 和 -`linear `_ 这两个模块进行张量并行操作。 +InternEvo 系统 ``v0.3.0`` 版本在张量并行策略上有较大更新,目前的张量并行支持四种模式配置['mtp', 'msp', 'fsp', 'isp'],前三种模式为基于 Megatron-LM 的张量并行和序列化并行的策略实现,最后一种模式为 InternEvo 系统自研,可与权重并行(Weight Parallel)结合使用的一种新的序列化并行方式。接下来详细介绍这几种张量并行模式的区别。 + +- MTP -用户可通过配置文件中的 ``parallel.tensor`` 字段来设置张量并行大小。 +MTP(Megatron-LM Tensor Parallel), 为默认的张量并行模型,引用自 `Megatron-LM Tensor Parallel `_ 并行方案,如下图所示,带有张量并行的 ``Transformer`` 层: -.. figure:: ../../imgs/tensor_parallel.png +.. figure:: ../../imgs/mtp.png :scale: 50% :class: with-border - 张量并行,采用自 `flash-attention `_ + Transformer layer with tensor parallelism. + +MTP 主要对 `attention `_ 和 `linear `_ 这两个模块进行张量并行操作。假设张量并行大小为 ``tp``,输入数据的序列长度为 ``seqlen``,隐藏层大小为 ``hidden size``,则张量并行期间产生的激活值 ``shape`` 为 ``[seqlen, hidden_size/tp]``。 + +MTP 张量并行引入的通信如上图所示,其中 ``f`` 和 ``f̄`` 是共轭的。在前向传递中 ``f`` 是无操作,而在反向传递中进行 ``all-reduce`` 操作。而 ``f̄`` 在前向传递中进行 ``all-reduce`` 操作,在反向传递中是无操作。 + +- MSP + +MSP(Megatron-LM Sequence Parallel),引用自 `Megatron-LM Sequence Parallel `_ 并行方案,如下图所示,带有张量并行和序列化并行的 ``Transformer`` 层: + +.. figure:: ../../imgs/msp.png + :scale: 50% + :class: with-border + + Transformer layer with tensor and sequence parallelism. + +与 MTP 对比,我们可以发现,MSP 主要针对未进行张量并行的模块,如 ``LayerNorm`` 和 ``Dropout`` 等模型进行序列化并行操作。需要注意的是,序列化并行大小与张量并行大小相等,且共用通信组。假设张量并行大小为 ``tp``,输入数据的序列长度为 ``seqlen``,隐藏层大小为 ``hidden size``,则序列化并行期间产生的激活值形状为 ``[seqlen/tp, hidden_size]``,张量并行期间产生的激活值形状为 ``[seqlen, hidden_size/tp]``。 + +MSP与MTP相比,通信原语有所变化,如上图所示 ``g`` 和 ``ḡ`` 是共轭的。在前向传递中 ``g`` 进行 ``all-gather`` 操作,而在反向传递中进行 ``reduce-scatter`` 操作。而 ``ḡ`` 在前向传递中进行 ``reduce-scatter`` 操作,在反向传递中进行 ``all-gather`` 操作。 + +在前向传递中 ``g`` 通信处于序列化并行和张量并行的交接处,进行的是激活值在 ``seqlen`` 维度的 ``all-gather`` 操作,该通信完成后,激活值形状变成完整的 ``[seqlen, hidden_size]``,然后进入张量并行模块范围。``ḡ`` 通信处于张量并行和序列化并行的交接处,需要把 MTP 中的 ``all-reduce`` 通信操作变成 ``reduce-scatter``,才能完成 ``seqlen`` 维度的切分,激活值形状变成 ``[seqlen/tp, hidden_size]``,从而正常进入序列化并行的阶段。而反向传递时,则是同样的道理。 + +- FSP + +FSP(Flash-Attn Sequence Parallel),引用自 `flash attention `_ 的序列化并行实现方案。该实现方案与 MSP 的唯一区别在于,在 ``g`` 进行 ``all-gather`` 通信后,MSP 会存储一份完整的输入数据用于 backward 计算,而 FSP 则只存储 ``seqlen`` 切分后的输入数据,因此在进行 backward 计算时,需要再额外 ``all-gather`` 一次输入。 + +因此,FSP 与 MSP 性能对比的话,会有更小的显存占用,但是由于引入额外的 ``all-gather`` 通信,会导致训练速度 TGS 降低。 + + +- ISP + +ISP(Intern Sequence Parallel),InternEvo 系统自研的灵活可扩展序列化并行方案,支持张量并行与序列化并行解耦,通过计算和通信的overlap提高训练性能,并基于显存池管理降低显存碎片化的可能性,提高显存利用率。 + +以 `configs/7B_isp_sft.py `_ 配置文件为例,将 ``tensor.mode`` 字段设置为 ``isp``,而 ``tensor.size`` 字段代表的是数据 ``seqlen`` 维度切分大小。ISP 算法可与 ``weight parallel`` 结合使用,其中 ``weight.size`` 字段代表的是模型权重切分大小,将 ``weight.overlap`` 字段设置为 ``True`` 即为开启计算与通信的 ``overlap``,可提高训练性能。将 ``weight.memory_pool`` 字段设置为 ``True`` 即为开启显存池管理功能,可一定程度降低 GPU 显存碎片的可能性,提高显存利用率。 + +.. code-block:: python + + parallel = dict( + zero1=dict(size=-1), + tensor=dict(size=2, mode="isp"), + pipeline=dict(size=1, interleaved_overlap=True), + weight=dict(size=4, overlap=True, memory_pool=True), + ) + +如下图所示,带有序列化并行和权重并行的 ``Transformer`` 层: + +.. figure:: ../../imgs/isp.png + :scale: 30% + :class: with-border + +如图所示,ISP 的序列化并行范围覆盖整个 ``Transformer`` 模型层,模型权重并行主要针对 ``Attention`` 和 ``MLP Block`` 的 ``Linear module``。 + +通信原语变化情况为,在前向传递时,每个 ``Linear`` 需要进行模型权重的 ``all-gather`` 通信;在后向传递时,每个 ``Linear`` 在进行后向计算前需要进行模型权重的 ``all-gather`` 通信,在后向计算后,需要进行模型权重的梯度的 ``reduce-scatter`` 通信操作。 + +需要注意的是,与 MSP 和 FSP 相比,在进行 ``attention score`` 计算时,ISP 也有通信原语的一些变化,如 ``Self-Atten`` 前后各增加了一个 ``all-to-all`` 通信操作,用于完成激活值形状的转置,目的是在进行 ``attention score`` 计算时能保持原有的张量并行的模式。 + +关于 ISP 算法更多的设计思路和性能评测,请参考论文 `InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding `_。 + 流水线并行 ----------------- @@ -55,7 +126,7 @@ InternEvo 在流水线并行中使用 `1F1B `_ @@ -97,20 +168,6 @@ InternEvo 在流水线并行中使用 `1F1B `_。这个并行策略有助于降低模型的内存消耗,提高了模型在资源受限环境中的可扩展性。 - -如果要启用序列并行, 用户需要设置 ``parallel.sequence_parallel = True``。 - -.. figure:: ../../imgs/sequence_parallel.png - :scale: 50% - :class: with-border - - 序列并行, 采用自 flash-attention - 数据并行 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