Comments (1)
The problem you're having is entire model is being loaded onto one or each TPU, and there is not enough memory on each one to do this.
The conversion script you linked was written for a single GPU (https://github.com/mlcommons/inference_results_v3.1/blob/951b4a7686692d1a0d9b9067a36a7fc26d72ada5/closed/Google/code/gptj-99/convert_gptj_ckpt.py#L154C1-L154C62), not a TPU cluster, for running the conversion, and so it will not shard without modification.
The offending line:
device_mesh = py_utils.create_device_mesh([1, 1, num_gpus])
This creates a device mesh of 1x1 with the number of GPUs (hardcoded to 1).
Try modifying it using the mesh sharding example from the JAX documentation: https://jax.readthedocs.io/en/latest/jax.sharding.html#jax.sharding.Mesh
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