Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

stack_llama: add parameter to control max_length (to mitigate OOM errors) #359

Merged
merged 1 commit into from
May 11, 2023
Merged

stack_llama: add parameter to control max_length (to mitigate OOM errors) #359

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 9 additions & 4 deletions examples/stack_llama/scripts/reward_modeling.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ class ScriptArguments:
default="linear",
metadata={"help": "The lr scheduler"},
)
max_length: Optional[int] = field(default=512)


parser = HfArgumentParser(ScriptArguments)
Expand Down Expand Up @@ -186,14 +187,18 @@ def preprocess_function(examples):
return new_examples


# preprocess the dataset and filter out QAs that are longer than 512
# preprocess the dataset and filter out QAs that are longer than script_args.max_length
train_dataset = train_dataset.map(
preprocess_function, batched=True, num_proc=num_proc, remove_columns=original_columns
)
train_dataset = train_dataset.filter(lambda x: len(x["input_ids_j"]) <= 512 and len(x["input_ids_k"]) <= 512)
train_dataset = train_dataset.filter(
lambda x: len(x["input_ids_j"]) <= script_args.max_length and len(x["input_ids_k"]) <= script_args.max_length
)

eval_dataset = eval_dataset.map(preprocess_function, batched=True, num_proc=num_proc, remove_columns=original_columns)
eval_dataset = eval_dataset.filter(lambda x: len(x["input_ids_j"]) <= 512 and len(x["input_ids_k"]) <= 512)
eval_dataset = eval_dataset.filter(
lambda x: len(x["input_ids_j"]) <= script_args.max_length and len(x["input_ids_k"]) <= script_args.max_length
)


# We need to define a special data collator that batches the data in our j vs k format.
Expand Down Expand Up @@ -276,7 +281,7 @@ def compute_loss(self, model, inputs, return_outputs=False):
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
data_collator=RewardDataCollatorWithPadding(tokenizer=tokenizer, max_length=512),
data_collator=RewardDataCollatorWithPadding(tokenizer=tokenizer, max_length=script_args.max_length),
)

trainer.train(script_args.resume_from_checkpoint)
Expand Down