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tests/workers/config/test_optim_config_on_cpu.py

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+# Copyright 2025 Bytedance Ltd. and/or its affiliates
+#
+# 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 pytest
+
+from verl.workers.config.optimizer import FSDPOptimizerConfig
+
+
+class TestFSDPOptimizerConfigCPU:
+    def test_default_configuration(self):
+        config = FSDPOptimizerConfig(lr=0.1)
+        assert config.min_lr_ratio is None
+        assert config.lr_scheduler_type == "constant"
+        assert config.num_cycles == 0.5
+
+    @pytest.mark.parametrize("lr_scheduler_type", ["constant", "cosine"])
+    def test_valid_lr_scheduler_types(self, lr_scheduler_type):
+        config = FSDPOptimizerConfig(lr_scheduler_type=lr_scheduler_type, lr=0.1)
+        assert config.lr_scheduler_type == lr_scheduler_type
+
+    @pytest.mark.parametrize("warmup_style", ["constant", "cosine"])
+    def test_valid_warmup_style_types(self, warmup_style):
+        config = FSDPOptimizerConfig(warmup_style=warmup_style, lr=0.1)
+        assert config.lr_scheduler_type == warmup_style
+
+    def test_invalid_lr_scheduler_type(self):
+        with pytest.raises((ValueError, AssertionError)):
+            FSDPOptimizerConfig(lr_scheduler_type="invalid_style", lr=0.1)
+
+    def test_invalid_warmup_style_type(self):
+        with pytest.raises((ValueError, AssertionError)):
+            FSDPOptimizerConfig(warmup_style="invalid_style", lr=0.1)
+
+    @pytest.mark.parametrize("num_cycles", [0.1, 1.0, 2.5])
+    def test_num_cycles_configuration(self, num_cycles):
+        config = FSDPOptimizerConfig(num_cycles=num_cycles, lr=0.1)
+        assert config.num_cycles == num_cycles