Size, Quality and Cost of Training Data in LLM’s
Size, Quality and Cost of Training Data in LLM’s
Training Dataset Size, Quality, Number of Training Parameters and Training Cost of a Large Language Model.
1. Size of the Training Data
First, we need to consider the size of the training data. It's quite simple: the larger the amount of text data the model is trained on, the more accurate and comprehensive its understanding of language will be. Bigger is generally better in this case, but there's more to it than just sheer volume of data as we’ll see next.
The exact size of the training dataset is not publicly known, but it is likely to be in the terabyte or petabyte range. This is a massive amount of data, and it required a significant amount of computational resources to train ChatGPT4.
The large training dataset allows ChatGPT4 to generate text that is more realistic and coherent than previous language models. It can also answer questions more accurately and generate more creative text formats.
Overall, the large training dataset of ChatGPT4 is a major advantage that allows it to generate more accurate and informative text. However, it also has some drawbacks that need to be considered.
The number of parameters in a language model is a measure of its complexity.
A model with more parameters can learn more complex relationships between words and phrases.
The training dataset for ChatGPT4 is estimated to be 100 trillion parameters, which is more than 5 times larger than the training data for ChatGPT-3.
This means that ChatGPT4 has been trained on a much larger and more diverse set of data, which allows it to generate more accurate and informative text.
Here are a few types of parameters in machine learning:
Model parameters are the values that are learned from the training data. They are the weights and biases of the model, and they determine how the model will make predictions.
Hyperparameters are the parameters that control the learning process. They are not learned from the training data, but they need to be set before the model can be trained.
Feature parameters are the values that represent the features of the data. They are typically used in conjunction with model parameters to make predictions.
Regularization parameters are used to prevent overfitting. They are typically added to the loss function, and they penalize the model for having large weights.
Here are some examples of each type of parameter:
Model parameters: The weights and biases of a neural network.
Hyperparameters: The learning rate, the number of epochs, and the batch size.
Feature parameters: The values of the features in the data set.
Regularization parameters: The L1 and L2 regularization coefficients.
The different types of parameters play different roles in machine learning. Model parameters are the most important, as they determine how the model will make predictions. Hyperparameters control the learning process, and they need to be set carefully to ensure that the model is not ‘Overfit’. Feature parameters represent the features of the data, and they are typically used in conjunction with model parameters to make predictions. Regularization parameters prevent ‘Overfitting’, and they can improve the performance of the model.
The size of the training dataset is typically measured in terabytes (TB) or petabytes (PB). (Note: A Terabyte is equal to 1,000 gigabytes (GB), and a Petabyte is equal to 1,000 Terabytes.)
The exact size of the training dataset for ChatGPT4 is not publicly known, but it is likely to be in the petabyte range.
The relationship between the number of parameters in a language model and the size of the training dataset is not linear.
A model with twice as many parameters does not require twice as much training data. However, in general, a larger training dataset will allow a model to learn more complex relationships between words and phrases, which can lead to improved performance.
However, the large size of the training dataset also has some drawbacks. It can make ChatGPT4 more difficult to train and fine-tune, and it can also make the model more prone to biases. The number of parameters in a model is a measure of its complexity. A model with more parameters can learn more complex relationships between the features and the target variable. However, a model with too many parameters can also be ‘Overfit’ to the training data, which means that it will not generalize well to new data.
In the case of ChatGPT4, the large training dataset has allowed the model to learn a wider range of vocabulary and grammar rules. This allows ChatGPT4 to generate more accurate and informative text, as well as to answer questions more comprehensively.
The large size of the training dataset also has some drawbacks. It can make ChatGPT4 more difficult to train and fine-tune, and it can also make the model more prone to biases. However, the benefits of a large training dataset outweigh the drawbacks, and ChatGPT4 is a powerful language model that can be used for a variety of tasks.
Qs? In GPT4, do all the one trillion parameters fall into one of these four categories or are there more categories?
Answer: The one trillion parameters in GPT-4 do not all fall into one of the four categories above. There are a few other categories of parameters that are used in GPT-4, which include:
Positional parameters: These parameters help the model understand the order of words in a sentence.
Attention parameters: These parameters allow the model to focus on specific parts of a sentence when making predictions.
Layer normalization parameters: These parameters help to normalize the activations of the model, which can improve the performance of the model.
Dropout parameters: These parameters allow the model to randomly drop out some of its connections, which can help to prevent overfitting.
In addition to these categories, there are also a few other types of parameters that are used in GPT-4. However, these parameters are less common and are not as important as the ones I have mentioned.
Overall, the parameters in GPT-4 are used to control the different aspects of the model. They allow the model to understand the order of words, focus on specific parts of a sentence, and make predictions. The different types of parameters play different roles in the model, and they are all important for the performance of the model.
Here are some of the most important and common parameters in GPT-4:
Model parameters: These parameters determine how the model will make predictions. They are the weights and biases of the model, and they are learned from the training data.
Positional parameters: These parameters help the model understand the order of words in a sentence. They are used to encode the position of each word in a sentence, which allows the model to understand the relationships between words.
Attention parameters: These parameters allow the model to focus on specific parts of a sentence when making predictions. They are used to compute the attention weights, which determine how much weight each word should be given when making a prediction.
Layer normalization parameters: These parameters help to normalize the activations of the model, which can improve the performance of the model. They are used to normalize the outputs of each layer in the model, which helps to prevent the model from becoming too excited or too depressed.
Dropout parameters: These parameters allow the model to randomly drop out some of its connections, which can help to prevent overfitting. They are used to randomly drop out some of the connections in the model, which prevents the model from becoming too dependent on any particular set of connections.
These are just some of the most important and common parameters in GPT-4. There are many other parameters that are used in the model, but these are some of the most important ones.
2. Quality of the Training Data
Which brings us to our second point, the quality of the training data. Here's where the adage 'garbage in, garbage out' really applies. The data needs to be accurate, relevant, and importantly, diverse. It should mirror the language used in real-world scenarios, and not be skewed or biased.
Qs? Most LLMs training is done on Datasets from Internet, Wikipedia, Books1, Books2, Wordnet etc. These datasets contain a lot of junk, garbage, fake information and even abusive stuff. How does this effect the quality of the data learned by the model?
Answer: The presence of junk, garbage, fake information, and abusive stuff in the datasets used to train large language models (LLMs) can have a number of negative effects on the quality of the data learned by the model.
First, it can cause the model to learn to generate text that is also junk, garbage, fake, or abusive. This can be a problem if the model is used to generate text that is intended to be informative or helpful. For example, if a model is trained on a dataset of Wikipedia articles, it may learn to generate text that is factually incorrect or misleading.
Second, the presence of junk, garbage, fake information, and abusive stuff in the training data can make it difficult for the model to learn to distinguish between good and bad information. This can be a problem if the model is used to answer questions or provide information. For example, if a model is trained on a dataset of news articles, it may not be able to tell the difference between a reliable news source and a fake news source.
Third, the presence of junk, garbage, fake information, and abusive stuff in the training data can make the model more likely to generate text that is offensive or harmful. This can be a problem if the model is used to interact with people, such as in a customer service setting. For example, if a model is trained on a dataset of social media posts, it may learn to generate text that is racist, sexist, or otherwise offensive.
There are a number of steps that can be taken to mitigate the negative effects of junk, garbage, fake information, and abusive stuff in the training data for LLMs. These include:
Filtering the training data: This involves removing any text that is obviously junk, garbage, fake, or abusive. This can be done manually or using automated tools.
Labeling the training data: This involves assigning labels to each piece of text in the training data, indicating whether it is good, bad, or neutral. This can help the model to learn to distinguish between good and bad information.
Using a variety of training datasets: This can help to reduce the impact of any individual dataset that contains junk, garbage, fake information, or abusive stuff.
Using regularization techniques: These techniques can help to prevent the model from learning to generate text that is too similar to the training data.
By taking these steps, it is possible to reduce the negative effects of junk, garbage, fake information, and abusive stuff in the training data for LLMs. This can help to ensure that the models generate text that is accurate, informative, and helpful.
3. Training Costs
Advances in software and hardware have reduced the cost substantially since 2020.
In 2023 training of a 12-billion-parameter LLM, the computational cost is 72,300 A100-GPU-hours. (Nvidia A100 GPU’s)
In 2020 the cost of training a 1.5-billion-parameter LLM (which was two orders of magnitude smaller than the state of the art in 2020) was between $80,000 and $1.6 million.
Since 2020, large sums were invested into increasingly large models. For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $8 million.
For Transformer-based LLM, training cost is much higher than inference cost. It costs 6 FLOPS per parameter to train on one token, whereas it costs 1 to 2 FLOPS per parameter to infer on one token.
(Note: FLOPS stands for Floating-Point Operations per Second. It is a measure of computer performance, useful in fields of scientific computations that require floating-point calculations. For such cases, it is a more accurate measure than measuring instructions per second.)
A floating-point operation is a mathematical operation that is performed on a floating-point number. Floating-point numbers are numbers that have a decimal point and can represent a wide range of values.
The FLOPS acronym is commonly misinterpreted as the plural form of "FLOP" (short for "Floating-Point operation"). FLOPS is a rate, not a quantity, and therefore does not have a plural form.
The number of FLOPS that a computer can perform per second is a measure of its floating-point performance. The higher the FLOPS rating, the faster the computer can perform floating-point operations.
FLOPS are often used to compare the performance of different computers. For example, a computer with a FLOPS rating of 100 GigaFLOPS (GFLOPS) is 100 billion times faster than a computer with a FLOPS rating of 1 MegaFLOPS (MFLOPS).
FLOPS are also used to measure the performance of different software applications. For example, a software application that performs a lot of floating-point calculations will require a computer with a high FLOPS rating to run efficiently.
Here are some examples of FLOPS ratings for different types of computers:
A typical desktop computer has a FLOPS rating of 100 GigaFLOPS (GFLOPS).
A supercomputer can have a FLOPS rating of 100 PetaFLOPS (PFLOPS).
A quantum computer can have a FLOPS rating of 100 ExaFLOPS (EFLOPS).
As you can see, the FLOPS rating can vary greatly depending on the type of computer.
Qs? In 2023 training of a 12-billion-parameter LLM, the computational cost is 72,300 A100-GPU-hours.What does this translate into Dollar costs?
Answer: The dollar cost of training a 12-billion-parameter LLM in 2023 depends on the cost of the A100 GPU-hours. As of June 2023, the cost of an A100 GPU-hour is about $0.25. Therefore, the dollar cost of training a 12-billion-parameter LLM is about $18,075.
Here is the calculation:
Cost of A100 GPU-hour = $0.25
Number of A100 GPU-hours required = 72,300
Cost of training LLM = 0.25 * 72,300 = $18,075
Note that the cost of training a 12-billion-parameter LLM is likely to decrease in the future as the cost of GPUs decreases. However, the dollar cost is still a significant factor to consider when training large language models.
The cost of one Nvidia A100 GPU in 2020 was $10,000. This was the starting price for the A100 GPU, and the price could vary depending on the manufacturer and the configuration of the GPU.
The A100 GPU is a high-performance GPU that was designed for deep learning applications. It is based on the NVIDIA Ampere architecture and it has 6912 CUDA cores.
The A100 GPU can achieve a peak performance of 19.5 teraflops of FP32 performance. (Note: CUDA, which stands for Compute Unified Device Architecture, Cores are the Nvidia GPU equivalent of CPU cores that have been designed to take on multiple calculations at the same time, which is significant when you're playing a graphically demanding game)
Qs? If it costs 6 FLOPs per parameter to train on one token, and it costs 1 to 2 FLOPs per parameter to ‘infer on one token’. What does it mean in terms of Dollar costs and If A100 GPU-hour cost is $0.25/hr, how many GPU's will be needed.
Answer: Here are the calculations for the dollar costs and number of GPUs needed to train and infer on a 12-billion-parameter LLM:
Training
Cost per parameter = 6 FLOPs
Number of tokens = 12 billion
Total FLOPs = 6 * 12 billion = 72 billion
GPU-hours required = 72 billion / 19.5 teraflops = 3,660 GPU-hours
Cost = 3,660 * $0.25 = $915
Inference
Cost per parameter = 2 FLOPs
Number of tokens = 12 billion
Total FLOPs = 2 * 12 billion = 24 billion
GPU-hours required = 24 billion / 19.5 teraflops = 1,200 GPU-hours
Cost = 1200 * $0.25 = $300
Therefore, the total cost of training and inferring on a 12-billion-parameter LLM is $1,215. The number of GPUs needed will depend on the specific hardware configuration, but it is likely to be around 20 GPUs.
Here are some additional considerations:
The cost of training and inferring on a 12-billion-parameter LLM will decrease as the cost of GPUs decreases. The number of GPUs needed will also decrease as the efficiency of GPUs increases.
The cost of training and inferring on a 12-billion-parameter LLM will also depend on the specific hardware configuration. For example, if the LLM is trained on a cluster of GPUs, the cost will be lower than if it is trained on a single GPU.
The A100 GPU was released in 2020 and it is still one of the most powerful GPUs available. It is used by a variety of companies for deep learning applications, including Google, Facebook, and Microsoft.
Qs? What does 'inference' mean if training?
Inference in machine learning is the process of using a trained model to make predictions on new data. It is the second phase of the machine learning lifecycle, after training.
Answer: During training, the model learns to associate features with labels. Once the model is trained, it can be used to make predictions on new data by finding the features that are most similar to the features in the training data.
For example, if a model is trained to classify images of cats and dogs, it can be used to make predictions on new images by finding the features that are most similar to the features in the training data. If the features of a new image are most similar to the features of cats in the training data, the model will predict that the image is a cat.
Inference is a critical part of the machine learning lifecycle. It allows models to be used to make predictions on new data, which can be used to solve a variety of problems.
The term "inference" can also be used to refer to the process of drawing conclusions from data. In this context, inference is a more general term that can be used to describe any process of making predictions or drawing conclusions from data.
In the context of machine learning, inference is typically used to refer to the process of making predictions on new data using a trained model. However, the term can also be used to refer to the process of drawing conclusions from data in general.
Qs: What does the statement; "The A100 GPU can achieve a peak performance of 19.5 teraflops of FP32 performance" mean?
Answer: It means that the A100 GPU can perform 19.5 trillion floating-point operations per second, in FP32 precision. Floating-point operations are the most common type of operation performed in machine learning and other computationally intensive workloads. FP32 precision is a single-precision floating-point format, which is the most common precision used in machine learning.
In other words, the A100 GPU is extremely fast at performing the types of calculations that are needed for machine learning and other computationally intensive workloads.
Here are some examples of what the A100 GPU can do with its 19.5 Teraflops of FP32 performance:
Train a large language model in minutes or hours
Generate realistic images and videos
Perform complex scientific simulations
Process large datasets in real time
The A100 GPU is a powerful tool for a wide range of applications, and its 19.5 teraflops of FP32 performance is one of its key features.
Qs: What is the peak performance of A100?
Answer: The double-precision FP64 performance is 9.7 TFLOPS, and with tensor cores this doubles to 19.5 TFLOPS. The single-precision FP32 performance is 19.5 TFLOPS and with the new Tensor Float (TF) precision this number significantly increases to 156 TFLOPS; ~20x higher than the previous generation V100.16
Qs: How many TeraFLOPS is A100 FP32?
Answer: The A100 GPU can achieve a peak performance of 19.5 teraflops of FP32 performance.
Qs: What do teraflops do in a GPU?
Answer: TFLOPS typically provide faster speeds and improved graphics. They can give intense speeds not seen in years past. Many devices couldn't approach one TFLOP level; today, 56 is a standard unit. The potential for supercomputers with over 100 Petaflops (one Petaflop is a thousand Teraflops) is becoming a reality.
Qs: What does the statement "The models training cost is 1.5 Peta-FLOP-day, which equals 100,000 Nvidia-A100 GPU hours." Mean?
Answer: It means that the model was trained on a machine with a peak performance of 1.5 petaflops, for one day. This is equivalent to 100,000 hours on an Nvidia-A100 GPU, which is one of the most powerful GPUs available.
Here is a breakdown of the terms:
FLOP: Floating-point operation per second. A FLOP is a measure of how many floating-point operations a computer can perform per second.
PetaFLOP: 10^15 FLOPs.
GPU: Graphics processing unit. A GPU is a specialized processor that is designed to perform parallel computations.
Nvidia-A100 GPU: One of the most powerful GPUs available, with a peak performance of 19.5 teraflops.
So, the statement is saying that the model was trained on a very powerful machine for a long time. This is necessary for training large and complex machine learning models.
Here are some examples of machine learning models that might require 1.5 Peta-FLOP-day of training:
Large language models, such as GPT-3 and LaMDA
Computer vision models, such as those used to recognize objects and faces in images
Natural language processing models, such as those used to translate languages and generate text
Reinforcement learning models, such as those used to train AI agents to play games
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