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Update style with ruff 0.2.2 (#1565)

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This is necessary to add to main fast, or else all branches from main
will require these changes to pass the quality checks.
This commit is contained in:
Benjamin Bossan
2024-03-15 10:20:41 +01:00
committed by GitHub
parent 6008f272a5
commit a18734d87a
6 changed files with 10 additions and 27 deletions
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8
examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py
View File

@ -260,9 +260,7 @@ def main():
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
train_epoch_loss = total_loss / len(train_dataloader)
train_ppl = torch.exp(train_epoch_loss)
@ -294,9 +292,7 @@ def main():
accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the eval (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the eval (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
correct = 0

8
examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py
View File

@ -211,9 +211,7 @@ def main():
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
train_epoch_loss = total_loss / len(train_dataloader)
train_ppl = torch.exp(train_epoch_loss)
@ -244,9 +242,7 @@ def main():
accelerator.print(f"CPU Memory consumed at the end of the eval (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the eval (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the eval (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the eval (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
correct = 0

8
examples/lora_dreambooth/train_dreambooth.py
View File

@ -1052,18 +1052,14 @@ def main(args):
accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
accelerator.print(
"GPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.peaked + b2mb(tracemalloc.begin)
)
f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
# Create the pipeline using using the trained modules and save it.

8
examples/oft_dreambooth/train_dreambooth.py
View File

@ -1060,18 +1060,14 @@ def main(args):
accelerator.print(f"GPU Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
accelerator.print(f"GPU Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
accelerator.print(
"GPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.peaked + b2mb(tracemalloc.begin)
)
f"GPU Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
)
accelerator.print(f"CPU Memory before entering the train : {b2mb(tracemalloc.cpu_begin)}")
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
# Create the pipeline using using the trained modules and save it.

4
examples/stable_diffusion/train_dreambooth.py
View File

@ -1225,9 +1225,7 @@ def main(args):
accelerator.print(f"CPU Memory consumed at the end of the train (end-begin): {tracemalloc.cpu_used}")
accelerator.print(f"CPU Peak Memory consumed during the train (max-begin): {tracemalloc.cpu_peaked}")
accelerator.print(
"CPU Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)
)
f"CPU Total Peak Memory consumed during the train (max): {tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin)}"
)
# Create the pipeline using using the trained modules and save it.

1
scripts/stale.py
View File

@ -15,6 +15,7 @@
Script to close stale issue. Taken in part from the AllenNLP repository.
https://github.com/allenai/allennlp.
"""
import os
from datetime import datetime as dt
from datetime import timezone