Script to compute step duration #70
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Precursor to #67. Imports https://github.com/AI-Hypercomputer/tpu-recipes/blob/main/utils/profile_convert.py and improves it. Specifically, I noticed sometimes there is an empty gap between two step markers in the profile. So if we averaged event durations, that would overestimate the MFU. Instead, this now averages the delta between the starting time offset of neighboring events. Now that we can print step time from the profile, I removed the step time from the training loop. That added a bunch of delays and is actually pretty inaccurate (1.7s vs 1.85s in local testing). Tested: XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 python3 torchprime/torch_xla_models/train.py mesh.fsdp=8 profile_step=4 model=llama-3-8b XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 python3 torchprime/torch_xla_models/train.py mesh.fsdp=8 profile_step=4 model=mixtral-8x7b
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Precursor to solving #67.
Imports https://github.com/AI-Hypercomputer/tpu-recipes/blob/main/utils/profile_convert.py and improves it.
Specifically, I noticed sometimes there is an empty gap between two step markers in the profile. So if we averaged event durations, that would overestimate the MFU. Instead, this now averages the delta between the starting time offset of neighboring events.
Now that we can print step time from the profile, I removed the step time from the training loop. That added a bunch of delays and is actually pretty inaccurate (1.7s vs 1.85s in local testing).
Tested:
XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 python3 torchprime/torch_xla_models/train.py mesh.fsdp=8 profile_step=4 model=llama-3-8b
XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 python3 torchprime/torch_xla_models/train.py mesh.fsdp=8 profile_step=4 model=mixtral-8x7b