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prevent prompt tensors from accumulating in GPU #84

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prevent prompt tensors from accumulating in GPU #84

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39 changes: 22 additions & 17 deletions inference/bot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,23 +85,28 @@ def __init__(self, model_name, gpu_id, max_memory):

def do_inference(self, prompt, max_new_tokens, do_sample, temperature, top_k, stream_callback=None):
stop_criteria = StopWordsCriteria(self._tokenizer, [self.human_id], stream_callback)
inputs = (
self._tokenizer(prompt, return_tensors='pt')
.to(self._model.device)
)
outputs = self._model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
pad_token_id=self._tokenizer.eos_token_id,
stopping_criteria=StoppingCriteriaList([stop_criteria]),
)
output = self._tokenizer.batch_decode(outputs)[0]

# remove the context from the output
output = output[len(prompt):]
try:
inputs = (
self._tokenizer(prompt, return_tensors='pt')
.to(self._model.device)
)
outputs = self._model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
pad_token_id=self._tokenizer.eos_token_id,
stopping_criteria=StoppingCriteriaList([stop_criteria]),
)
del inputs
output = self._tokenizer.batch_decode(outputs)[0]
del outputs
Comment on lines +102 to +104
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Thanks, @MichaMucha. Should del inputs and del outputs be in the finally block instead?

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Apologies for the delay-
I suppose in case outputs failed to generate for some reason, they wouldn't be set, therefore del outputs in the finally block would throw an exception..

Not sure if a similar case can be made for inputs, but I wanted to avoid the risk of an unhandled exceptio

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Not sure if a similar case can be made for inputs, but I wanted to avoid the risk of an unhandled exceptio

Makes sense, 'outputs' isn't defined if it errors out before then. Maybe just move inputs?

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Can we check if inputs and outputs are set before del inputs and del outputs in the finally block?


# remove the context from the output
output = output[len(prompt):]
finally:
torch.cuda.empty_cache()
Comment on lines +88 to +109
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Suggested change
try:
inputs = (
self._tokenizer(prompt, return_tensors='pt')
.to(self._model.device)
)
outputs = self._model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
pad_token_id=self._tokenizer.eos_token_id,
stopping_criteria=StoppingCriteriaList([stop_criteria]),
)
del inputs
output = self._tokenizer.batch_decode(outputs)[0]
del outputs
# remove the context from the output
output = output[len(prompt):]
finally:
torch.cuda.empty_cache()
inputs = None
outputs = None
try:
inputs = (
self._tokenizer(prompt, return_tensors='pt')
.to(self._model.device)
)
outputs = self._model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
pad_token_id=self._tokenizer.eos_token_id,
stopping_criteria=StoppingCriteriaList([stop_criteria]),
)
output = self._tokenizer.batch_decode(outputs)[0]
# remove the context from the output
output = output[len(prompt):]
finally:
if inputs is not None:
del inputs
if outputs is not None:
del outputs
torch.cuda.empty_cache()

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@MichaMucha ☝️


return output

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