Table of contents
1. What difficulties will a large model development project face?
4. What are the difficulties in training large models?
5. What are the solutions for distributed training?
1. What difficulties will a large model development project face?
As the technical leader of a large model development project, you may face some of the following challenges and difficulties:
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Data Acquisition and Processing : Acquiring large amounts of high-quality data is an important but challenging task during the development of large models. You need to identify data sources, deal with data privacy and security issues, and deal with big data storage and processing capacity issues.
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Model design and training : Selecting and designing an appropriate model structure and reasonable model parameters is an important step. In addition, for large models, the training time may be very long, you need to have enough computing resources and suitable hardware facilities.
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Overfitting and underfitting : Large models sometimes face the problem of overfitting, where the model performs well on the training set but poorly on the validation and test sets. Underfitting means that the model does not perform well on the training set. To solve these problems, you may need to use regularization techniques, early stopping, etc.
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Model Interpretation and Transparency : Large models often act like a "black box" and it is difficult to understand its inner workings. This can lead to problems where the model's predictions are hard to interpret, or the model's behavior in certain situations is hard to predict.
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Model Deployment and Maintenance : Deploying a trained model to production is a challenging task. You need to consider the performance of the model and how to integrate the model with existing systems and architectures. Additionally, you will need to maintain and update your model regularly to ensure its continued performance and accuracy.
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Model ethics and fairness issues : If the training data for a model is biased, then the model's predictions may also be biased. Therefore, you need to ensure that the model is fair and respects the user's privacy when processing data.
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Resource management : Managing and scheduling computing resources (such as GPUs, CPUs, memory, etc.) to meet the needs of large-scale model training is an important task. In addition, the timeline and budget of the project need to be considered.
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Teamwork : As a technical lead, you need to coordinate and manage a multidisciplinary team, including data scientists, engineers, product managers, etc. Effective communication and teamwork are key to project success.
2. I need to train a model with 10 billion parameters, how do I estimate the required computing resources?
Estimating the computing resources required to train a 10 billion parameter model is a complex issue that requires consideration of many factors, such as the model structure, the size of the training data set, and the number of training iterations. The following is a rough framework:
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Parameter storage : First, it is necessary to estimate the storage space required by the model parameters. In general, each parameter is usually represented by a 32-bit (that is, 4 bytes) floating-point number. Therefore, 10 billion parameters requires about 40GB of storage. But during the training process, you may also need to store additional information, such as gradients, optimizer state, etc., so the actual storage space required may be larger.
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Computational Power : Estimating the required computational power is complex because it depends on the structure of the model and the training algorithm. You need to estimate the computational complexity of each training iteration, and then multiply by the number of training iterations. For some models, such as Transformer, the complexity is proportional to the square of the length of the input sequence. For some other models, such as convolutional neural networks, the complexity is proportional to the size of the input.
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Training time : Once you estimate the computing power required for each training iteration, you can estimate the total training time by multiplying it by the number of training iterations. You can then estimate the actual training time based on the performance of your hardware (e.g. FLOPS).
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Memory : Model parameters and intermediate calculation results need to be stored in memory, which also requires a lot of memory resources. You need to consider the size of the model, the batch size, and possible memory optimization techniques (such as gradient checkpointing).
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Data transfer : If you are using distributed training, then data transfer can also be a bottleneck. You need to consider the transfer time of data between different devices.
The above is only a rough estimation framework, and the actual computing resource requirements may vary according to factors such as specific hardware devices, software tools, and optimization techniques. In addition, some experiments may be needed to assist in the estimation process.
3. During the training process, how to calculate the video memory space required to store the gradient?
In the process of training a deep learning model, in order to calculate the backpropagation to update the parameters, it is necessary to save the gradient information in memory. A gradient is saved for each parameter, which requires as much storage as the parameters themselves.
If your model has 10 billion parameters, then in theory you need about the same amount of storage to store those gradients. In practice, the actual storage requirements may be higher due to various reasons (e.g. optimizer state, intermediate outputs of activation functions, etc.).
Specifically, if you are using 32-bit floating point numbers (single precision floating point numbers, aka float32), then each parameter and each gradient will take up 4 bytes of space. So, for 10 billion parameters, you need about 40GB to store the parameters, plus 40GB to store the gradients, for a total of 80GB.
However, this is only the minimum required to save parameters and gradients. Additional storage may be required if you use an optimizer with extra state, such as Adam or RMSProp. Also, if you use a training strategy that requires storing intermediate activations (for example, if you use standard backpropagation or some type of regularization), then additional space is required.
Finally, if video memory is insufficient, there are strategies that can help reduce memory usage, such as using mixed-precision training, using gradient checkpointing, or using model parallelism.
4. What are the difficulties in training large models?
Training large models is indeed a complex challenge, involving many algorithmic and engineering aspects. Here are some difficulties you may encounter:
Algorithmic level:
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Optimization Difficulties : Larger models have more parameters, which can make optimization more difficult. For example, you may encounter problems with vanishing or exploding gradients, which can cause training to be unstable or fail to converge.
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Overfitting : Large models have greater capacity, which makes them easier to overfit to the training data. While there are some techniques to prevent overfitting, such as regularization, early stopping, dropout, etc., these methods may not always be effective.
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Generalization : Large models can be more difficult to generalize to new data. While large models fit the training data well, they may not perform well on new, unseen data.
Engineering level:
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Computing resources : Training large models requires a lot of computing resources, including CPU, GPU, memory, and storage. This may require high-performance hardware devices and efficient resource management.
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Storage and memory requirements : Large models require more storage space to store model parameters, and more memory space to store intermediate calculation results. This can cause storage and memory to become bottlenecks.
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Training time : Due to the computational complexity of large models, training time can be very long. You may need to use distributed training or more efficient optimization algorithms to speed up training.
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Stability and reliability : The training of large models may involve massive parallel and distributed computations. This can cause stability and reliability issues such as hardware failures, network issues, etc.
The above are some of the difficulties that training large models may encounter. Of course, these difficulties are not insurmountable. There are many researches and technologies being devoted to solving these problems, such as more efficient optimization algorithms, more powerful hardware devices, smarter resource management, etc.
5. What are the solutions for distributed training?
Distributed training is an important method for dealing with large-scale data and large models, which can significantly reduce training time. Here are some major distributed training schemes, along with their ideas, advantages and disadvantages:
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Data Parallelism : This is the most commonly used distributed training method. In data parallelism, each processor has a full copy of the model, and each processor works on a different part of the input data. Then, each processor computes gradients on its data, and the gradients are aggregated across all processors. Finally, each processor uses this aggregated gradient to update its copy of the model.
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Advantages: Easy to use, can effectively use multiple GPUs or CPUs for training, and can directly reduce training time.
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Cons: Since each processor needs to have a full copy of the model, it can be limited by the memory size of the processor. Furthermore, gradient communication can become a bottleneck in a large-scale distributed setting.
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Model Parallelism : In model parallelism, the model is divided into parts and each processor processes one part of the model. This approach is suitable for cases where a single model is too large to be fully loaded on one processor.
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Pros: Allows training large models beyond the limits of a single processor's memory.
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Cons: Model parallelism requires fine-grained model partitioning and communication across processors, which can lead to increased complexity. Also, if parts of the model are unbalanced, some processors may sit idle while waiting for others, which reduces efficiency.
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Pipeline Parallelism : Pipeline parallelism is a variant of model parallelism in which different layers of the model are processed on different processors. After each processor completes the calculation of one layer, it passes the result to the next processor.
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Pros: Allows efficient balancing of workloads among processors, can reduce communication overhead.
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Cons: It may require a more complex programming model and may be limited by pipeline depth. In addition, it may also require a finer-grained scheduling strategy to reduce idle time.
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Gradient Accumulation : In gradient accumulation, each processor processes a different part of the data and computes gradients independently. Then, each processor accumulates its gradients into a global gradient, and finally uses this global gradient to update the model.
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Advantages: This method can reduce the number of gradient communications, thereby reducing communication overhead. In addition, it can also make each processor work independently, which makes it more scalable in large-scale distributed settings.
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Disadvantage: Gradient accumulation may require more computing resources, because each processor needs to process more data. Furthermore, it may increase the instability of training, since the computation of the global gradient may be affected by noise.
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The above are some of the main distributed training solutions. It should be noted that these solutions can be used in combination according to specific needs and constraints. For example, data parallelism and model parallelism can be used together to take full advantage of the computational power and memory of multiple processors.
6. Multi-machine multi-card parallel training, how is the gradient between the machine and the card integrated?
Data Parallel:
In multi-machine multi-card data parallel training, each GPU will have a copy of the model, and each GPU will independently process a subset of the input data. Each GPU computes the gradients of the model based on its own subset of data, and then these gradients need to be fused together for updating the model parameters. Gradient fusion is usually carried out in two ways: parameter server (Parameter Server) and ring communication (Ring Communication).
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Parameter Server : In a parameter server architecture, there are one or more dedicated parameter servers whose task is to store model parameters and handle gradient updates. Each GPU will send its computed gradients to the parameter server, which then sums all the gradients to calculate the average gradient, and finally the parameter server uses this average gradient to update the model parameters . The parameter server then sends the updated model parameters back to each GPU.
- Advantages: This method is simple and intuitive, easy to understand and implement.
- Cons: The parameter server can become a performance bottleneck, especially when the number of GPUs increases.
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Ring communication : Ring communication is a more efficient gradient fusion strategy. In ring communication, each GPU communicates directly with two other GPUs (a "left neighbor" and a "right neighbor"). Each GPU first sends its computed gradients to its right neighbor while receiving gradients from its left neighbor. Each GPU then adds the received gradients to its own and sends the result to its right neighbor. This process is repeated until each GPU has received the gradients of all other GPUs.
- Pros: This approach reduces communication bottlenecks and can fuse gradients more efficiently in large-scale distributed settings.
- Cons: This approach can be more complex to implement than a parameter server, requiring more careful synchronization and scheduling.
Model parallelism:
In model parallel training, a large model is divided into multiple parts, and each processor (such as a GPU card) is responsible for a part of the model. This approach is useful when a single model is too large to fully load on one processor.
Different from gradient fusion in data parallel training, in the case of model parallel training, each processor only processes and updates a part of the model, so gradient fusion is not required. Specifically, each processor computes the outputs (i.e., activations) for its part of the model during the forward pass and passes these outputs to the next processor. Then, during backpropagation, each processor receives the gradients computed by its subsequent processors and, based on these gradients and the activations saved during its forward pass, computes the gradients for the part of the model it is responsible for. Finally, each processor uses these gradients to update the parameters of the part of the model it is responsible for.
It should be noted here that although each processor only processes and updates a part of the model, all processors need to share the same optimizer state (such as learning rate, momentum, etc.). This may require additional synchronization operations, such as all processors needing to update their optimizer state after each parameter update.
Therefore, in model parallel training, different machines and cards do not need to fuse gradients or parameters. Instead, they each process and update a part of the model, and complete forward propagation and back propagation through network communication collaboration. The end result is a complete, updated model with different parts of the model spread across different processors.