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CI / KTOTrainer: Remove old tests #1750

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Jun 18, 2024
Merged

CI / KTOTrainer: Remove old tests #1750

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4f40528
remove old tests
younesbelkada Jun 17, 2024
df7991a
remove datasets
younesbelkada Jun 17, 2024
4f1c419
Update test_dpo_trainer.py
younesbelkada Jun 18, 2024
184b274
Update test_dpo_trainer.py
younesbelkada Jun 18, 2024
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6 changes: 3 additions & 3 deletions tests/test_dpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ def setUpClass(cls):
cls.tokenizer.pad_token = cls.tokenizer.eos_token

# get t5 as seq2seq example:
model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration-correct-vocab"
model_id = "trl-internal-testing/T5ForConditionalGeneration-correct-vocab-calibrated"
cls.t5_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
cls.t5_ref_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
cls.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -125,8 +125,8 @@ def test_dpo_trainer(self, name, loss_type, pre_compute):
ref_model = self.t5_ref_model
tokenizer = self.t5_tokenizer

if name == "t5" and loss_type == "nca_pair":
self.skipTest("For some reason t5 + nca_pair does not compute gradients properly on tiny models")
if name == "t5":
self.skipTest("For some reason t5 does not compute gradients properly on tiny models")

trainer = DPOTrainer(
model=model,
Expand Down
122 changes: 0 additions & 122 deletions tests/test_kto_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,74 +82,6 @@ def _init_dummy_dataset(self):
# fmt: on
return Dataset.from_dict(dummy_dataset_dict)

def _init_dummy_dataset_only_desirable(self):
# fmt: off
dummy_dataset_unbalanced_dict = {
"prompt": [
"Hey, hello",
"How are you",
"What is your name?",
"What is your name?",
"Which is the best programming language?",
"Which is the best programming language?",
"Which is the best programming language?",
],
"completion": [
"hi nice to meet you",
"leave me alone",
"I don't have a name",
"My name is Mary",
"Python",
"C++",
"Java",
],
"label": [
True,
True,
True,
True,
True,
True,
True,
],
}
# fmt: on
return Dataset.from_dict(dummy_dataset_unbalanced_dict)

def _init_dummy_dataset_no_desirable(self):
# fmt: off
dummy_dataset_unbalanced_dict = {
"prompt": [
"Hey, hello",
"How are you",
"What is your name?",
"What is your name?",
"Which is the best programming language?",
"Which is the best programming language?",
"Which is the best programming language?",
],
"completion": [
"hi nice to meet you",
"leave me alone",
"I don't have a name",
"My name is Mary",
"Python",
"C++",
"Java",
],
"label": [
False,
False,
False,
False,
False,
False,
False,
],
}
# fmt: on
return Dataset.from_dict(dummy_dataset_unbalanced_dict)

@parameterized.expand(
[
["gpt2", "kto", True, True],
Expand Down Expand Up @@ -212,60 +144,6 @@ def test_kto_trainer(self, name, loss_type, pre_compute, eval_dataset):
if param.sum() != 0:
self.assertFalse(torch.equal(param, new_param))

@require_no_wandb
def test_kto_trainer_no_desirable_input(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
remove_unused_columns=False,
)

dummy_dataset = self._init_dummy_dataset_no_desirable()

model = self.model
ref_model = self.ref_model
tokenizer = self.tokenizer

with self.assertRaises(
ValueError,
msg="The set of desirable completions cannot be empty.",
):
_ = KTOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=None,
)

@require_no_wandb
def test_kto_trainer_only_desirable_input(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
remove_unused_columns=False,
)

dummy_dataset = self._init_dummy_dataset_only_desirable()

model = self.model
ref_model = self.ref_model
tokenizer = self.tokenizer

with self.assertRaises(
ValueError,
msg="The set of undesirable completions cannot be empty.",
):
_ = KTOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=None,
)

def test_tokenize_and_process_tokens(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
Expand Down
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