CI / KTOTrainer: Remove old tests (#1750)

* remove old tests * remove datasets * Update test_dpo_trainer.py * Update test_dpo_trainer.py
huggingface · Jun 18, 2024 · 83b367b · 83b367b
1 parent d1ed730
commit 83b367b
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Showing 2 changed files with 3 additions and 125 deletions.
diff --git a/tests/test_dpo_trainer.py b/tests/test_dpo_trainer.py
@@ -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)
@@ -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,

diff --git a/tests/test_kto_trainer.py b/tests/test_kto_trainer.py
@@ -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],
@@ -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(