# Copyright (c) 2025 PaddlePaddle Authors. 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 unittest import paddle from paddle.distributed import fleet from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.dygraph_sharding_optimizer import ( DygraphShardingOptimizerV2, ) class TestClearParamStorage(unittest.TestCase): def test_clear_param_storage(self): class TestLayer(paddle.nn.Layer): def __init__(self, dtype): super().__init__() self._w = self.create_parameter([2, 3], dtype=dtype) self._b = self.create_parameter([2, 3], dtype=dtype) self._w.color = {"color": "_w"} self._b.color = {"color": "_b"} @paddle.amp.debugging.check_layer_numerics def forward(self, x): return x * self._w + self._b strategy = fleet.DistributedStrategy() strategy.hybrid_configs = { "dp_degree": 1, "mp_degree": 1, "pp_degree": 1, "sharding_degree": 2, } fleet.init(is_collective=True, strategy=strategy) hcg = fleet.get_hybrid_communicate_group() dtype = 'float32' model = TestLayer(dtype) optimizer = paddle.optimizer.AdamW(parameters=model.parameters()) optimizer = DygraphShardingOptimizerV2(optimizer, hcg) optimizer.clear_param_storage("_w") optimizer.clear_param_storage("_b") optimizer.clear_param_storage(None) optimizer.reset_param_storage() if __name__ == '__main__': unittest.main()