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To estimate the total number of FLOPs required to train GPT-3 without searching online, we can make several assumptions and perform calculations based on the information available about GPT-3's architecture and training process. Here's a step-by-step explanation of the reasoning:
Step 1: Determine the number of parameters in GPT-3
GPT-3 is a massive language model with 175 billion parameters. Each parameter represents a trainable weight in the neural network, and updating these weights during training requires floating-point operations (FLOPs).
Step 2: Estimate the number of training iterations
The number of training iterations refers to how many times the entire training dataset is passed through the neural network during the training process. Although the exact number may vary, let's assume that GPT-3 was trained for 100 epochs.
Step 3: Calculate the FLOPs per iteration
During each training iteration, the neural network performs forward and backward passes. The forward pass involves computing the output of the network given the input, while the backward pass involves calculating the gradients of the loss function with respect to the network's weights.
For simplicity, we can assume that the forward and backward passes have roughly the same number of FLOPs. Therefore, the total FLOPs per iteration can be estimated by multiplying the number of parameters by 2 (forward and backward passes).
Total FLOPs per iteration = (Number of parameters) x 2
(175 billion parameters) x 2 = 350 billion FLOPs per iteration
Step 4: Calculate the total FLOPs for training
Multiplying the FLOPs per iteration by the number of training iterations gives us the total FLOPs required to train GPT-3.
Total FLOPs for training = (FLOPs per iteration) x (Number of iterations)
(350 billion FLOPs per iteration) x (100 iterations) = 35 trillion FLOPs for training
Therefore, based on these assumptions, we estimate that training GPT-3 without searching online would require approximately 35 trillion FLOPs.