Replace unittest with pytest

tests/slow/test_dpo_slow.py

@@ -21,12 +21,12 @@
 from datasets import load_dataset
 from parameterized import parameterized
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from transformers.testing_utils import backend_empty_cache, require_peft, require_torch_accelerator, torch_device
+from transformers.testing_utils import backend_empty_cache, require_torch_accelerator, torch_device
 from transformers.utils import is_peft_available
 
 from trl import DPOConfig, DPOTrainer
 
-from ..testing_utils import TrlTestCase, require_bitsandbytes
+from ..testing_utils import TrlTestCase, require_bitsandbytes, require_peft
 from .testing_constants import DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST
 
 
@@ -37,9 +37,8 @@
 @pytest.mark.slow
 @require_torch_accelerator
 @require_peft
-class DPOTrainerSlowTester(TrlTestCase):
-    def setUp(self):
-        super().setUp()
+class TestDPOTrainerSlow(TrlTestCase):
+    def setup_method(self):
         self.dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
         self.peft_config = LoraConfig(
             lora_alpha=16,
@@ -50,11 +49,10 @@ def setUp(self):
         )
         self.max_length = 128
 
-    def tearDown(self):
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
         gc.collect()
-        super().tearDown()
 
     @parameterized.expand(list(itertools.product(MODELS_TO_TEST, DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS)))
     def test_dpo_bare_model(self, model_id, loss_type, pre_compute_logits):
@@ -151,8 +149,8 @@ def test_dpo_peft_model(self, model_id, loss_type, pre_compute_logits, gradient_
             peft_config=self.peft_config,
         )
 
-        self.assertIsInstance(trainer.model, PeftModel)
-        self.assertIsNone(trainer.ref_model)
+        assert isinstance(trainer.model, PeftModel)
+        assert trainer.ref_model is None
 
         # train the model
         trainer.train()
@@ -215,8 +213,8 @@ def test_dpo_peft_model_qlora(self, model_id, loss_type, pre_compute_logits, gra
             peft_config=self.peft_config,
         )
 
-        self.assertIsInstance(trainer.model, PeftModel)
-        self.assertIsNone(trainer.ref_model)
+        assert isinstance(trainer.model, PeftModel)
+        assert trainer.ref_model is None
 
         # train the model
         trainer.train()

tests/slow/test_grpo_slow.py

@@ -35,7 +35,6 @@
     backend_empty_cache,
     require_flash_attn,
     require_liger_kernel,
-    require_peft,
     require_torch_accelerator,
     torch_device,
 )
@@ -44,7 +43,7 @@
 from trl import GRPOConfig, GRPOTrainer
 from trl.trainer.utils import get_kbit_device_map
 
-from ..testing_utils import TrlTestCase, require_bitsandbytes, require_vllm
+from ..testing_utils import TrlTestCase, require_bitsandbytes, require_peft, require_vllm
 from .testing_constants import MODELS_TO_TEST
 
 
@@ -54,18 +53,16 @@
 
 @pytest.mark.slow
 @require_torch_accelerator
-class GRPOTrainerSlowTester(TrlTestCase):
-    def setUp(self):
-        super().setUp()
+class TestGRPOTrainerSlow(TrlTestCase):
+    def setup_method(self):
         self.train_dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
         self.eval_dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="test")
         self.max_length = 128
 
-    def tearDown(self):
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
         gc.collect()
-        super().tearDown()
 
     @parameterized.expand(MODELS_TO_TEST)
     @require_liger_kernel
@@ -103,7 +100,7 @@ def test_training_with_liger_grpo_loss(self, model_name):
 
         for n, param in previous_trainable_params.items():
             new_param = model.get_parameter(n)
-            self.assertFalse(torch.equal(param, new_param), f"Parameter {n} has not changed.")
+            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
 
         release_memory(model, trainer)
 
@@ -153,20 +150,20 @@ def test_training_with_liger_grpo_loss_and_peft(self, model_name):
         # Verify PEFT adapter is properly initialized
         from peft import PeftModel
 
-        self.assertTrue(isinstance(trainer.model, PeftModel), "Model should be wrapped with PEFT")
+        assert isinstance(trainer.model, PeftModel), "Model should be wrapped with PEFT"
 
         # Store adapter weights before training
         previous_trainable_params = {
             n: param.clone() for n, param in trainer.model.named_parameters() if param.requires_grad
         }
-        self.assertTrue(len(previous_trainable_params) > 0, "No trainable parameters found in PEFT model")
+        assert len(previous_trainable_params) > 0, "No trainable parameters found in PEFT model"
 
         trainer.train()
 
         # Verify adapter weights have changed after training
         for n, param in previous_trainable_params.items():
             new_param = trainer.model.get_parameter(n)
-            self.assertFalse(torch.equal(param, new_param), f"Parameter {n} has not changed.")
+            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
 
         release_memory(model, trainer)
 
@@ -199,12 +196,12 @@ def test_training_with_transformers_paged(self, model_name):
 
         trainer.train()
 
-        self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+        assert trainer.state.log_history[-1]["train_loss"] is not None
 
         # Check that the params have changed
         for n, param in previous_trainable_params.items():
             new_param = model.get_parameter(n)
-            self.assertFalse(torch.equal(param, new_param), f"Parameter {n} has not changed.")
+            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
 
         release_memory(model, trainer)
 
@@ -310,13 +307,13 @@ def reward_func(prompts, completions, **kwargs):
                 peft_config=lora_config,
             )
 
-            self.assertIsInstance(trainer.model, PeftModel)
+            assert isinstance(trainer.model, PeftModel)
 
             previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
 
             trainer.train()
 
-            self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+            assert trainer.state.log_history[-1]["train_loss"] is not None
 
             # Check that LoRA parameters have changed
             # For VLM models, we're more permissive about which parameters can change
@@ -328,7 +325,7 @@ def reward_func(prompts, completions, **kwargs):
                         lora_params_changed = True
 
             # At least some LoRA parameters should have changed during training
-            self.assertTrue(lora_params_changed, "No LoRA parameters were updated during training.")
+            assert lora_params_changed, "No LoRA parameters were updated during training."
 
         except torch.OutOfMemoryError as e:
             self.skipTest(f"Skipping VLM training test due to insufficient GPU memory: {e}")
@@ -378,8 +375,8 @@ def test_vlm_processor_vllm_colocate_mode(self):
         processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct", use_fast=True, padding_side="left")
 
         # Verify processor has both required attributes for VLM detection
-        self.assertTrue(hasattr(processor, "tokenizer"))
-        self.assertTrue(hasattr(processor, "image_processor"))
+        assert hasattr(processor, "tokenizer")
+        assert hasattr(processor, "image_processor")
 
         def dummy_reward_func(completions, **kwargs):
             return [1.0] * len(completions)
@@ -438,16 +435,14 @@ def dummy_reward_func(completions, **kwargs):
                     )
 
                     # Should detect VLM processor correctly and allow vLLM
-                    self.assertTrue(trainer.use_vllm, "vLLM should be enabled for VLM processors in colocate mode")
-                    self.assertEqual(trainer.vllm_mode, "colocate", "Should use colocate mode")
+                    assert trainer.use_vllm, "vLLM should be enabled for VLM processors in colocate mode"
+                    assert trainer.vllm_mode == "colocate", "Should use colocate mode"
 
                     # Check if signature columns were set properly
                     if trainer._signature_columns is not None:
                         # Should include 'image' in signature columns for VLM processors
-                        self.assertIn(
-                            "image",
-                            trainer._signature_columns,
-                            "Should include 'image' in signature columns for VLM",
+                        assert "image" in trainer._signature_columns, (
+                            "Should include 'image' in signature columns for VLM"
                         )
 
                     # Should not emit any warnings about VLM incompatibility
@@ -457,10 +452,8 @@ def dummy_reward_func(completions, **kwargs):
                         if "does not support VLMs" in str(w_item.message)
                         or "not compatible" in str(w_item.message).lower()
                     ]
-                    self.assertEqual(
-                        len(incompatibility_warnings),
-                        0,
-                        f"Should not emit VLM incompatibility warnings, but got: {incompatibility_warnings}",
+                    assert len(incompatibility_warnings) == 0, (
+                        f"Should not emit VLM incompatibility warnings, but got: {incompatibility_warnings}"
                     )
 
                     # Test passes if we get this far without exceptions
@@ -525,12 +518,12 @@ def test_training_vllm(self):
 
             trainer.train()
 
-            self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+            assert trainer.state.log_history[-1]["train_loss"] is not None
 
             # Check that the params have changed
             for n, param in previous_trainable_params.items():
                 new_param = trainer.model.get_parameter(n)
-                self.assertFalse(torch.equal(param, new_param), f"Parameter {n} has not changed.")
+                assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
 
         except Exception as e:
             # If vLLM fails to initialize due to hardware constraints or other issues, that's expected

tests/slow/test_sft_slow.py

@@ -24,7 +24,6 @@
 from transformers.testing_utils import (
     backend_empty_cache,
     require_liger_kernel,
-    require_peft,
     require_torch_accelerator,
     require_torch_multi_accelerator,
     torch_device,
@@ -33,7 +32,7 @@
 
 from trl import SFTConfig, SFTTrainer
 
-from ..testing_utils import TrlTestCase, require_bitsandbytes
+from ..testing_utils import TrlTestCase, require_bitsandbytes, require_peft
 from .testing_constants import DEVICE_MAP_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST, PACKING_OPTIONS
 
 
@@ -44,9 +43,8 @@
 @pytest.mark.slow
 @require_torch_accelerator
 @require_peft
-class SFTTrainerSlowTester(TrlTestCase):
-    def setUp(self):
-        super().setUp()
+class TestSFTTrainerSlow(TrlTestCase):
+    def setup_method(self):
         self.train_dataset = load_dataset("stanfordnlp/imdb", split="train[:10%]")
         self.eval_dataset = load_dataset("stanfordnlp/imdb", split="test[:10%]")
         self.max_length = 128
@@ -58,11 +56,10 @@ def setUp(self):
             task_type="CAUSAL_LM",
         )
 
-    def tearDown(self):
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
         gc.collect()
-        super().tearDown()
 
     @parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
     def test_sft_trainer_str(self, model_name, packing):
@@ -148,7 +145,7 @@ def test_sft_trainer_peft(self, model_name, packing):
             peft_config=self.peft_config,
         )
 
-        self.assertIsInstance(trainer.model, PeftModel)
+        assert isinstance(trainer.model, PeftModel)
 
         trainer.train()
 
@@ -252,7 +249,7 @@ def test_sft_trainer_transformers_mp_gc_peft(self, model_name, packing, gradient
             peft_config=self.peft_config,
         )
 
-        self.assertIsInstance(trainer.model, PeftModel)
+        assert isinstance(trainer.model, PeftModel)
 
         trainer.train()
 
@@ -332,7 +329,7 @@ def test_sft_trainer_transformers_mp_gc_peft_qlora(self, model_name, packing, gr
             peft_config=self.peft_config,
         )
 
-        self.assertIsInstance(trainer.model, PeftModel)
+        assert isinstance(trainer.model, PeftModel)
 
         trainer.train()
 
@@ -372,7 +369,7 @@ def test_sft_trainer_with_chat_format_qlora(self, model_name, packing):
             peft_config=self.peft_config,
         )
 
-        self.assertIsInstance(trainer.model, PeftModel)
+        assert isinstance(trainer.model, PeftModel)
 
         trainer.train()
 
@@ -447,11 +444,11 @@ def test_train_offloading(self, model_name, packing):
         trainer.train()
 
         # Check that the training loss is not None
-        self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+        assert trainer.state.log_history[-1]["train_loss"] is not None
 
         # Check the params have changed
         for n, param in previous_trainable_params.items():
             new_param = trainer.model.get_parameter(n)
-            self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
+            assert not torch.allclose(param, new_param), f"Parameter {n} has not changed"
 
         release_memory(trainer.model, trainer)

tests/test_activation_offloading.py

@@ -16,12 +16,12 @@
 import torch
 from torch import nn
 from transformers import AutoModelForCausalLM
-from transformers.testing_utils import require_peft, require_torch_accelerator, torch_device
+from transformers.testing_utils import require_torch_accelerator, torch_device
 from transformers.utils import is_peft_available
 
 from trl.models.activation_offloading import NoOpManager, OffloadActivations
 
-from .testing_utils import TrlTestCase
+from .testing_utils import TrlTestCase, require_peft
 
 
 if is_peft_available():
@@ -72,9 +72,8 @@ def test_offloading_with_peft_models(self) -> None:
         for name_orig, grad_orig in grads_original:
             for name_param, param in model.named_parameters():
                 if name_param == name_orig and param.requires_grad and param.grad is not None:
-                    self.assertTrue(
-                        torch.allclose(grad_orig, param.grad, rtol=1e-4, atol=1e-5),
-                        f"Gradient mismatch for {name_orig}",
+                    assert torch.allclose(grad_orig, param.grad, rtol=1e-4, atol=1e-5), (
+                        f"Gradient mismatch for {name_orig}"
                     )
 
     @require_torch_accelerator
@@ -105,7 +104,7 @@ def test_noop_manager_with_offloading(self):
 
         # Gradients should match as NoOpManager should have prevented offloading
         for g1, g2 in zip(grads1, grads2):
-            self.assertTrue(torch.allclose(g1, g2, rtol=1e-4, atol=1e-5))
+            assert torch.allclose(g1, g2, rtol=1e-4, atol=1e-5)
 
     @require_torch_accelerator
     def test_min_offload_size(self):
@@ -152,6 +151,6 @@ def test_real_hf_model(self):
         grads2 = [p.grad.clone() for p in model.parameters()]
 
         # Check outputs and gradients match
-        self.assertTrue(torch.allclose(out1, out2, rtol=1e-5))
+        assert torch.allclose(out1, out2, rtol=1e-5)
         for g1, g2 in zip(grads1, grads2):
-            self.assertTrue(torch.allclose(g1, g2, rtol=1e-5))
+            assert torch.allclose(g1, g2, rtol=1e-5)