To perform tests on the ported Mojo code, execute the following command:
magic shell
mojo test_gpt2.mojoThis script is a Mojo port of the original test_gpt2.c, created by Andrej. and replicates the testing functionality from the C version.
The testing process involves loading the gpt2_124M_debug_state.bin file and running a forward pass to compare the computed logits and loss with the reference values obtained from the PyTorch implementation. Additionally, the test performs 10 iterations of training using the Adam optimizer to verify that the losses match those computed by PyTorch.
When running the test, the losses align with the PyTorch results within the specified accuracy range. However, some logits and parameter tensors display discrepancies. These discrepancies stem from changes in the order of operations caused by the implemented vectorization. Given the non-associative nature of floating-point arithmetic, such changes can lead to variations in outcomes. For more details on why floating-point arithmetic can lead to such discrepancies, see Floating-point arithmetic on Wikipedia.
Discrepancies in the logits and parameter tensors are noted; however, they do not reflect a decrease in quality. The primary indicator of model performance, the training losses, aligns closely with the PyTorch benchmarks. This consistent alignment verifies the reliability and precision of the Mojo implementation, affirming its functional equivalence with the original C version.