张量并行

AI领域是当今科技发展最前沿的阵地之一，而大型AI模型，特别是大型语言模型（LLMs），正以惊人的速度演进。然而，这些庞大模型的训练和部署对计算资源提出了巨大的挑战，单个计算设备（如GPU）往往无法承载。为了突破这一瓶颈，科学家和工程师们发展出了一系列巧妙的并行计算策略，其中“张量并行”（Tensor Parallelism）便是举足轻重的一员。

第一章：什么是“张量”？万物皆数

在深入探讨“张量并行”之前，我们首先需要理解什么是“张量”。对于非专业人士来说，我们可以把“张量”理解为多维的数字数组。

  • 标量（0维张量）： 最简单，就是一个独立的数字，比如你的年龄“30”。
  • 向量（1维张量）： 就是一个数字列表，比如你今天吃的三餐花费清单：。
  • 矩阵（2维张量）： 更像一个表格，有行有列，比如一个班级所有学生语文和数学成绩的列表。
  • 高维张量（3维或更高维）： 就像一张彩色照片，它有宽度、高度，还有一个深度（代表红、绿、蓝三种颜色通道）。或者像一部电影，它是由连续的照片（3维张量）序列组成的，就增加了一个时间维度。

在AI的世界里，所有的数据——无论是输入的文本、图片，还是模型内部的各种参数（比如神经元的连接权重），甚至是中间计算结果，都是以张量的形式存在的。因此，AI的计算本质上就是张量与张量之间的运算。

第二章：为什么需要并行计算？一个人掰不过来！

随着AI模型变得越来越“聪明”，它们的规模也越来越庞大，参数数量动辄达到几十亿、几千亿甚至上万亿。模型越大，意味着它内部需要存储的“数字”（张量）越多，计算时需要处理的“数字运算”也越复杂。

想象一下，你有一本厚达一万页的百科全书，并且需要在一分钟内找出其中所有提到“人工智能”这个词的页面，并总结这些内容。如果只有你一个人，即使你是世界上最快的阅读者，也几乎不可能完成。当前大部分高性能的GPU虽然很强大，但它们的内存（能记住多少内容）和计算能力也是有限的。当模型大到某个程度，一个GPU无论是储存模型参数还是进行计算，都会“力不从心”，甚至直接“内存溢出”而崩溃。为了解决这个问题，分布式训练技术应运而生，其中的核心思想就是——并行计算。

第三章：并行计算的“老搭档”——数据并行与模型并行

为了让多个计算设备协同工作，AI领域发展出了多种并行策略。我们先简单认识两种与张量并行经常一起使用的策略：

  1. 数据并行（Data Parallelism）：
    想象一家大型蛋糕店，接到了一百个一模一样的蛋糕订单。最简单的做法是：雇佣十个糕点师，每个糕点师都拥有一份完整的蛋糕配方和烤箱，然后每人负责制作十个蛋糕。
    在AI训练中，这意味着每个GPU都拥有模型的一个完整副本，然后将训练数据分成小份，每个GPU处理一份数据，独立进行计算。最后，所有GPU计算出的结果（梯度）进行平均，更新模型。这种方式简单高效，但前提是每个GPU都能完整装下整个模型。

  2. 模型并行（Model Parallelism）：
    当订单量太大，或者某个蛋糕非常复杂，一个糕点师做不完，甚至一个烤箱都装不下时，数据并行就失效了。模型并行则像一条流水线：第一个糕点师完成蛋糕的第一步（比如和面），然后传递给第二个糕点师进行第二步（发酵），再给第三个糕点师进行第三步（烘烤），以此类推。
    在AI中，模型并行就是将模型的不同部分（比如不同的层）分配到不同的GPU上，每个GPU只负责模型的一部分计算。数据会按顺序在这些GPU之间流转，完成整个模型的计算。流水线并行（Pipeline Parallelism）就是模型并行的一种常见形式。

然而，如果蛋糕的某一个步骤本身就非常复杂，比如“烘烤”这个步骤需要一个巨大且复杂的烤箱，且其内部的温度控制和加热方式无法被单个设备完成，那该怎么办呢？这时，就需要“张量并行”登场了。

第四章：揭秘张量并行：把一道超级大菜的“烹饪”部分拆开做！

张量并行 演示

张量并行是模型并行的一种特殊且更为细粒度的形式。它的核心思想是：将模型内部一个巨大的“张量运算”（比如一个大的矩阵乘法）拆分成多个小部分，让不同的GPU同时处理这些小部分，最终再将结果合并起来。

让我们用一个形象的比喻来解释：

想象你和你的团队正在为一面超级巨大的、需要特殊质感的墙进行涂色。这面墙大到一个人根本无法独立完成，甚至一块小区域的涂色也需要非常精密的计算和协调。

  • 张量并行的方法： 你的团队决定不再是一个人涂一整块小墙，也不是一个人涂一道工序。而是把这面超级大墙横向或者纵向地“切分”成几块，每个团队成员（GPU）负责涂自己分到的那“一块”墙面。更重要的是，他们是同时在“同一层工序”上并行工作。比如，完成“底漆”这道工序时，多名工人同时动手，各自负责一部分墙面。

具体到AI中的矩阵乘法（这是AI模型中最常见的运算之一）：
假设我们要计算一个矩阵乘法 Y = X * W，其中 X 是输入张量，W 是模型权重张量，Y 是输出张量。如果 W 矩阵非常大，一个GPU无法存储或计算：

  1. 切分思路： 我们可以将 W 矩阵（或 X 矩阵）沿着某一维度进行切分。例如，将 W 矩阵按列切分成 W1 和 W2，分别存储在GPU1和GPU2上。
  2. 并行计算： GPU1计算 Y1 = X * W1，GPU2计算 Y2 = X * W2。这两个计算可以同时进行。
  3. 结果合并： 最后，将GPU1计算出的 Y1 和GPU2计算出的 Y2 合并起来，就得到了完整的输出 Y。这个合并过程通常通过一种称为“All-reduce”或“All-gather”的通信操作来完成，确保所有GPU都能获得完整或协调的结果。

这种方式相当于在模型内部的某个特定运算环节，将运算任务和相关的张量（数据和权重）分解开来，由多个设备协同完成。NVIDIA的Megatron-LM框架是张量并行技术的先驱之一，它尤其针对Transformer模型中的自注意力机制和多层感知机（MLP）等关键部分进行了拆分并行。 DeepSpeed等其他主流框架也集成了Megatron-LM的张量并行实现，并持续优化其效率。

第五章：张量并行的优缺点

优点：

  • 突破内存限制： 最大的优势在于它能将巨大的模型参数张量分担到多个GPU上，使得单个GPU可以不必存储整个模型，从而训练和部署超大规模模型成为可能。
  • 加速计算： 通过在层内进行并行计算，可以显著加速模型的前向和反向传播过程。
  • 支持更大批次： 特别是二维甚至多维张量并行，可以有效减少激活值（中间计算结果）的内存占用，从而允许训练时使用更大的批量大小（Batch Size），这通常有助于提高训练效果。

缺点：

  • 通信开销大： 由于需要频繁地在多个GPU之间传输切分后的张量和合并结果，通信开销会比较大。这要求设备之间有高速的网络连接。
  • 实现复杂： 相较于数据并行，张量并行的实现要复杂得多，需要根据模型结构和张量维度的特点进行细致的切分设计和通信策略。
  • 通用性挑战： 早期的一些张量并行方案（如Megatron-LM的1D张量并行）主要针对Transformer架构，不具备完全的通用性，并可能在激活值内存占用上仍有不足。为此，更先进的2D、2.5D、3D张量并行方案被提出，以解决这些问题。

第六章：张量并行的实际应用与未来展望

如今，张量并行已经成为大型语言模型（LLMs）训练和推理不可或缺的关键技术。像GPT系列这样参数规模惊人的模型，其训练离不开张量并行的支持。 无论是训练（如Megatron-LM、DeepSpeed、Colossal-AI等框架提供的支持），还是部署推理（大模型推理也面临单卡显存不足的挑战），张量并行都发挥着至关重要的作用。

随着AI模型规模的持续膨胀，以及对更高性能和效率的追求，未来的张量并行技术将继续演进。例如，结合张量并行、流水线并行和ZeRO等数据并行优化技术，形成“3D并行”策略，已经成为训练超大规模模型的有效手段。 此外，如何进一步优化通信，并在各种硬件架构上实现高效且通用的张量并行，仍是AI系统领域持续研究的热点。

结语

张量并行不是魔法，它是AI工程师们为了应对模型爆炸式增长带来的计算和内存挑战所采取的精密策略。通过将模型内部的复杂计算“大卸八块”，再让多个GPU协同作战，张量并行如同一个高效的“数字化流水线”，让训练和部署那些改变世界的AI巨兽成为可能。理解它，便能更好地理解AI大模型背后的工程之美。

Chapter 1: What is a “Tensor”? Everything is a Number

Before diving into “Tensor Parallelism,” we first need to understand what a “tensor” is. For non-professionals, we can understand a “tensor” as a multi-dimensional array of numbers.

  • Scalar (0-dimensional tensor): The simplest, just an independent number, like your age “30”.
  • Vector (1-dimensional tensor): Just a list of numbers, like your spending list for three meals today: .
  • Matrix (2-dimensional tensor): More like a table, with rows and columns, such as a list of Chinese and Math scores for all students in a class.
  • High-dimensional Tensor (3-dimensional or higher): Like a color photo, it has width, height, and a depth (representing red, green, and blue color channels). Or like a movie, which is composed of a sequence of continuous photos (3-dimensional tensors), adding a time dimension.

In the world of AI, all data—whether input text, images, or various parameters inside the model (such as connection weights of neurons), and even intermediate calculation results—exist in the form of tensors. Therefore, AI calculation is essentially operations between tensors.

Chapter 2: Why Do We Need Parallel Computing? One Person Can’t Handle It!

As AI models become “smarter,” their scale also becomes larger, with the number of parameters often reaching tens of billions, hundreds of billions, or even trillions. The larger the model, the more “numbers” (tensors) it needs to store internally, and the more complex the “numerical operations” it needs to process during calculation.

Imagine you have an encyclopedia that is ten thousand pages thick, and you need to find all the pages mentioning the word “Artificial Intelligence” and summarize the content within one minute. If you are alone, even if you are the fastest reader in the world, it is almost impossible to complete. Although most current high-performance GPUs are powerful, their memory (how much content they can remember) and computing power are also limited. When the model is large to a certain extent, a GPU will be “powerless” whether storing model parameters or performing calculations, or even crash directly due to “out of memory.” To solve this problem, distributed training technology came into being, and its core idea is—Parallel Computing.

Chapter 3: The “Old Partner” of Parallel Computing—Data Parallelism and Model Parallelism

To make multiple computing devices work together, the AI field has developed various parallel strategies. Let’s first briefly understand two strategies often used together with tensor parallelism:

  1. Data Parallelism:
    Imagine a large cake shop receives one hundred identical cake orders. The simplest way is: hire ten pastry chefs, each with a complete cake recipe and oven, and then each person is responsible for making ten cakes.
    In AI training, this means that each GPU has a complete copy of the model, and then the training data is divided into small portions, with each GPU processing one portion of data and performing calculations independently. Finally, the results (gradients) calculated by all GPUs are averaged to update the model. This method is simple and efficient, but the premise is that each GPU can completely hold the entire model.

  2. Model Parallelism:
    When the order volume is too large, or a certain cake is very complex, and one pastry chef cannot finish it, or even one oven cannot hold it, data parallelism fails. Model parallelism is like an assembly line: the first pastry chef completes the first step of the cake (such as mixing dough), then passes it to the second pastry chef for the second step (fermentation), and then to the third pastry chef for the third step (baking), and so on.
    In AI, model parallelism is to distribute different parts of the model (such as different layers) to different GPUs, with each GPU only responsible for part of the model’s calculation. Data will flow between these GPUs in sequence to complete the calculation of the entire model. Pipeline Parallelism is a common form of model parallelism.

However, if a certain step of the cake itself is very complex, for example, the “baking” step requires a huge and complex oven, and its internal temperature control and heating method cannot be completed by a single device, what should be done? At this time, “Tensor Parallelism” needs to come on stage.

Chapter 4: Revealing Tensor Parallelism: Splitting the “Cooking” Part of a Super Big Dish!

Parallel Computing Demo

Tensor parallelism is a special and more fine-grained form of model parallelism. Its core idea is: Split a huge “tensor operation” (such as a large matrix multiplication) inside the model into multiple small parts, let different GPUs process these small parts simultaneously, and finally merge the results.

Let’s use a vivid metaphor to explain:

Imagine you and your team are painting a super huge wall that requires a special texture. This wall is so big that one person cannot complete it independently, and even painting a small area requires very precise calculation and coordination.

  • Method of Tensor Parallelism: Your team decides not to let one person paint a whole small wall, nor one person paint a process. Instead, they “slice” this super big wall horizontally or vertically into several pieces, and each team member (GPU) is responsible for painting the “piece” of wall assigned to them. More importantly, they are working in parallel on the “same process” at the same time. For example, when completing the “primer” process, multiple workers start at the same time, each responsible for a part of the wall.

Specific to Matrix Multiplication in AI (This is one of the most common operations in AI models):
Suppose we want to calculate a matrix multiplication Y = X * W, where X is the input tensor, W is the model weight tensor, and Y is the output tensor. If the W matrix is very large, a single GPU cannot store or calculate it:

  1. Splitting Idea: We can split the W matrix (or X matrix) along a certain dimension. For example, split the W matrix by columns into W1 and W2, stored on GPU1 and GPU2 respectively.
  2. Parallel Calculation: GPU1 calculates Y1 = X * W1, and GPU2 calculates Y2 = X * W2. These two calculations can be performed simultaneously.
  3. Result Merging: Finally, merge Y1 calculated by GPU1 and Y2 calculated by GPU2 to get the complete output Y. This merging process is usually completed through a communication operation called “All-reduce” or “All-gather” to ensure that all GPUs can obtain complete or coordinated results.

This method is equivalent to decomposing the computing task and related tensors (data and weights) in a specific computing link inside the model, and completing it collaboratively by multiple devices. NVIDIA’s Megatron-LM framework is one of the pioneers of tensor parallelism technology, which specifically splits and parallelizes key parts such as the self-attention mechanism and multi-layer perceptron (MLP) in the Transformer model. Other mainstream frameworks such as DeepSpeed also integrate the tensor parallelism implementation of Megatron-LM and continuously optimize its efficiency.

Chapter 5: Pros and Cons of Tensor Parallelism

Pros:

  • Break Memory Limits: The biggest advantage is that it can distribute huge model parameter tensors to multiple GPUs, making it unnecessary for a single GPU to store the entire model, thus making it possible to train and deploy ultra-large-scale models.
  • Accelerate Calculation: By performing parallel calculations within layers, the forward and backward propagation processes of the model can be significantly accelerated.
  • Support Larger Batches: Especially 2D or even multi-dimensional tensor parallelism can effectively reduce the memory usage of activation values (intermediate calculation results), thereby allowing larger batch sizes during training, which usually helps improve training effects.

Cons:

  • High Communication Overhead: Due to the frequent transmission of split tensors and merged results between multiple GPUs, the communication overhead can be relatively large. This requires high-speed network connections between devices.
  • Complex Implementation: Compared with data parallelism, the implementation of tensor parallelism is much more complex, requiring detailed splitting design and communication strategies based on the characteristics of the model structure and tensor dimensions.
  • Generality Challenge: Some early tensor parallelism schemes (such as Megatron-LM’s 1D tensor parallelism) mainly target the Transformer architecture and do not have complete generality, and may still have deficiencies in activation value memory usage. To this end, more advanced 2D, 2.5D, and 3D tensor parallelism schemes have been proposed to solve these problems.

Chapter 6: Practical Application and Future Outlook of Tensor Parallelism

Today, tensor parallelism has become an indispensable key technology for the training and inference of Large Language Models (LLMs). Models with amazing parameter scales like the GPT series cannot be trained without the support of tensor parallelism. Whether it is training (supported by frameworks such as Megatron-LM, DeepSpeed, Colossal-AI) or deployment inference (large model inference also faces the challenge of insufficient single-card video memory), tensor parallelism plays a vital role.

With the continuous expansion of AI model scale and the pursuit of higher performance and efficiency, future tensor parallelism technology will continue to evolve. For example, combining tensor parallelism, pipeline parallelism, and data parallelism optimization technologies such as ZeRO to form a “3D parallelism” strategy has become an effective means for training ultra-large-scale models. In addition, how to further optimize communication and achieve efficient and general tensor parallelism on various hardware architectures remains a hot spot for continuous research in the field of AI systems.

Conclusion

Tensor parallelism is not magic; it is a precise strategy adopted by AI engineers to cope with the computing and memory challenges brought about by the explosive growth of models. By “dismembering” the complex calculations inside the model and then letting multiple GPUs fight together, tensor parallelism is like an efficient “digital assembly line,” making it possible to train and deploy those AI giants that change the world. Understanding it will help you better understand the engineering beauty behind large AI models.