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Drop setUpClass in reward tester #1895

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merged 5 commits into from
Aug 5, 2024
Merged

Drop setUpClass in reward tester #1895

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85cefe2
drop setUp class in reward tester
qgallouedec Aug 2, 2024
d1f40b7
Merge branch 'main' into drop_setup_in_test
qgallouedec Aug 2, 2024
eec4870
report to none
qgallouedec Aug 3, 2024
b7e91dd
Merge branch 'drop_setup_in_test' of https://github.com/huggingface/t…
qgallouedec Aug 3, 2024
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style
qgallouedec Aug 3, 2024
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drop setUp class in reward tester
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@qgallouedec
qgallouedec committed Aug 2, 2024
commit 85cefe29a35c2a06431307a8c58bc9052651d569
55 changes: 34 additions & 21 deletions tests/test_reward_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,19 26,16 @@


class RewardTrainerTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
cls.model = AutoModelForSequenceClassification.from_pretrained(cls.model_id)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.tokenizer.pad_token = cls.tokenizer.eos_token

def test_accuracy_metrics(self):
dummy_eval_predictions = EvalPrediction(torch.FloatTensor([[0.1, 0.9], [0.9, 0.1]]), torch.LongTensor([0, 0]))
accuracy = compute_accuracy(dummy_eval_predictions)
assert accuracy["accuracy"] == 0.5

def test_reward_trainer(self):
model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
Expand Down Expand Up @@ -81,9 78,9 @@ def test_reward_trainer(self):
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)

trainer = RewardTrainer(
model=self.model,
model=model,
args=training_args,
tokenizer=self.tokenizer,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
Expand All @@ -108,6 105,11 @@ def test_reward_trainer(self):
def test_reward_trainer_peft(self):
from peft import LoraConfig, TaskType

model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

peft_config = LoraConfig(
task_type=TaskType.SEQ_CLS,
inference_mode=False,
Expand Down Expand Up @@ -158,9 160,9 @@ def test_reward_trainer_peft(self):
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)

trainer = RewardTrainer(
model=self.model,
model=model,
args=training_args,
tokenizer=self.tokenizer,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
peft_config=peft_config,
Expand Down Expand Up @@ -196,6 198,10 @@ def test_reward_trainer_peft(self):
assert preds.predictions.shape == (4, 2)

def test_reward_trainer_assert_value_error(self):
model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
Expand Down Expand Up @@ -235,9 241,9 @@ def test_reward_trainer_assert_value_error(self):
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)

trainer = RewardTrainer(
model=self.model,
model=model,
args=training_args,
tokenizer=self.tokenizer,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
)

Expand All @@ -253,13 259,17 @@ def test_reward_trainer_assert_value_error(self):

with self.assertWarns(UserWarning):
trainer = RewardTrainer(
model=self.model,
model=model,
args=training_args,
tokenizer=self.tokenizer,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
)

def test_reward_trainer_margin(self):
model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
Expand Down Expand Up @@ -293,24 303,27 @@ def test_reward_trainer_margin(self):
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)

trainer = RewardTrainer(
model=self.model,
model=model,
args=training_args,
tokenizer=self.tokenizer,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)

batch = [dummy_dataset[0]]
batch = trainer.data_collator(batch)
batch = {k: v.to(trainer.model.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
loss, outputs = trainer.compute_loss(trainer.model, batch, return_outputs=True)

l_val = -torch.nn.functional.logsigmoid(
outputs["rewards_chosen"] - outputs["rewards_rejected"] - batch["margin"]
).mean()

assert abs(loss - l_val) < 1e-6

def test_reward_trainer_tags(self):
model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
Expand Down Expand Up @@ -353,9 366,9 @@ def test_reward_trainer_tags(self):
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)

trainer = RewardTrainer(
model=self.model,
model=model,
args=training_args,
tokenizer=self.tokenizer,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
Expand Down
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