Move tests for GSPOTokenTrainer to experimental

tests/experimental/test_gspo_token_trainer.py

@@ -0,0 +1,60 @@
+# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch
+from datasets import load_dataset
+from transformers.utils import is_peft_available
+
+from trl import GRPOConfig
+from trl.experimental.gspo_token import GRPOTrainer as GSPOTokenTrainer
+
+from ..testing_utils import TrlTestCase
+
+
+if is_peft_available():
+    pass
+
+
+class TestGSPOTokenTrainer(TrlTestCase):
+    def test_training(self):
+        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
+
+        training_args = GRPOConfig(
+            output_dir=self.tmp_dir,
+            learning_rate=0.1,  # increase the learning rate to speed up the test
+            per_device_train_batch_size=3,  # reduce the batch size to reduce memory usage
+            num_generations=3,  # reduce the number of generations to reduce memory usage
+            max_completion_length=8,  # reduce the completion length to reduce memory usage
+            num_iterations=2,  # the importance sampling weights won't be 0 in this case
+            importance_sampling_level="sequence_token",
+            report_to="none",
+        )
+        trainer = GSPOTokenTrainer(
+            model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            reward_funcs="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
+            args=training_args,
+            train_dataset=dataset,
+        )
+
+        previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
+
+        trainer.train()
+
+        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)
+            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

tests/test_grpo_trainer.py

@@ -36,7 +36,6 @@
 from transformers.utils import is_peft_available
 
 from trl import GRPOConfig, GRPOTrainer
-from trl.experimental.gspo_token import GRPOTrainer as GSPOTokenTrainer
 from trl.trainer.utils import get_kbit_device_map
 
 from .testing_utils import (
@@ -1799,39 +1798,6 @@ def test_single_reward_model_with_single_processing_class(self):
         assert trainer.reward_processing_classes[0] == single_processing_class
 
 
-class TestGSPOTokenTrainer(TrlTestCase):
-    def test_training(self):
-        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
-
-        training_args = GRPOConfig(
-            output_dir=self.tmp_dir,
-            learning_rate=0.1,  # increase the learning rate to speed up the test
-            per_device_train_batch_size=3,  # reduce the batch size to reduce memory usage
-            num_generations=3,  # reduce the number of generations to reduce memory usage
-            max_completion_length=8,  # reduce the completion length to reduce memory usage
-            num_iterations=2,  # the importance sampling weights won't be 0 in this case
-            importance_sampling_level="sequence_token",
-            report_to="none",
-        )
-        trainer = GSPOTokenTrainer(
-            model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
-            reward_funcs="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
-            args=training_args,
-            train_dataset=dataset,
-        )
-
-        previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
-
-        trainer.train()
-
-        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)
-            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
-
-
 @pytest.mark.slow
 @require_torch_accelerator
 class TestGRPOTrainerSlow(TrlTestCase):