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No need to call xm.mark_step() explicitly #4

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Nov 21, 2019
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No need to call xm.mark_step() explicitly #4

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1 change: 0 additions & 1 deletion examples/run_glue_tpu.py
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Expand Up @@ -150,7 +150,6 @@ def train(args, train_dataset, model, tokenizer, disable_logging=False):
loss = outputs[0] # model outputs are always tuple in transformers (see doc)

if args.gradient_accumulation_steps > 1:
xm.mark_step() # Mark step to evaluate graph so far or else graph will grow too big and OOM.
loss = loss / args.gradient_accumulation_steps

loss.backward()
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