[v1] support ulysses cp for fsdp2 (#10262)

tests_v1/plugins/model_plugins/test_ulysses_cp.py

@@ -0,0 +1,62 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# 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
+import torch
+import torch.multiprocessing as mp
+
+from llamafactory.v1.accelerator.interface import DistributedInterface
+from llamafactory.v1.config.model_args import ModelArguments
+from llamafactory.v1.core.model_engine import ModelEngine
+from llamafactory.v1.plugins.model_plugins.parallelization.sequence_parallel import (
+    SequenceParallelModelPlugin,
+    sequence_parallel_loss,
+)
+from llamafactory.v1.utils.env import find_available_port
+from llamafactory.v1.utils.pytest import dist_env
+
+
+def _test_sequence_parallel_loss(local_rank: int, world_size: int, master_port: int, cp_size: int, dp_size: int):
+    with dist_env(local_rank, world_size, master_port):
+        model_args = ModelArguments(model="llamafactory/tiny-random-qwen3")
+
+        # Initialize distributed interface with config
+        dist_config = {"cp_mode": "ulysses", "cp_size": cp_size, "dp_size": dp_size}
+        DistributedInterface(dist_config)
+
+        # Now create model engine
+        model_engine = ModelEngine(model_args=model_args)
+
+        # Apply sequence parallel plugin
+        SequenceParallelModelPlugin(dist_config.get("cp_mode", "ulysses"))(model_engine.model, dist_config)
+
+        model_inputs = {
+            "input_ids": torch.tensor([[1, 2, 3, 4, 5]]),
+            "labels": torch.tensor([[1, 2, 3, 4, 5]]),
+            "attention_mask": torch.tensor([[1, 1, 1, 1, 1]]),
+            "position_ids": torch.tensor([[1, 2, 3, 4, 5]]),
+            "loss_weights": torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0]]),
+        }
+
+        loss = sequence_parallel_loss(model_engine.model, model_inputs)
+        assert loss is not None
+
+
+@pytest.mark.runs_on(["cuda", "npu"])
+@pytest.mark.require_distributed(2)
+@pytest.mark.parametrize("cp_size, dp_size", [(2, 1)])
+def test_sequence_parallel_loss(cp_size, dp_size):
+    master_port = find_available_port()
+    world_size = cp_size * dp_size
+    mp.spawn(_test_sequence_parallel_loss, args=(world_size, master_port, cp_size, dp_size), nprocs=world_size)