wehub-resource-sync
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.distributed import fleet
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from paddle.distributed.fleet.layers.mpu import (
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ColumnParallelLinear,
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)
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from paddle.nn import Layer
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class SimpleMLP(Layer):
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def __init__(self, in_features=1024, out_features=1024):
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super().__init__()
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self.linear = ColumnParallelLinear(
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in_features, out_features, has_bias=False
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)
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def forward(self, x):
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x = self.linear(x)
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return x
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class TestLoadStateDictCastLogic:
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def __init__(self):
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self.aoa_config = {"aoa_statements": [os.getenv("aoa_statements")]}
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self.ckpt_path = tempfile.TemporaryDirectory().name
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self.in_features = 1024
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self.out_features = 2048
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def run_test(self):
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self.run_save_state_dict()
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model = SimpleMLP()
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model_cast = SimpleMLP()
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model_cast = paddle.amp.decorate(
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models=model_cast,
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optimizers=None,
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level="O2",
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dtype="float16",
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)
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sharded_state_dict = model.sharded_state_dict()
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sharded_state_dict_trans = model_cast.sharded_state_dict()
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dist.load_state_dict(sharded_state_dict, self.ckpt_path)
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dist.load_state_dict(
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sharded_state_dict_trans, self.ckpt_path, aoa_config=self.aoa_config
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)
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state_dict_1_after_load = model.state_dict()
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state_dict_2_after_load = model_cast.state_dict()
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np.testing.assert_array_equal(
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state_dict_1_after_load['linear.weight'].astype("float16"),
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state_dict_2_after_load['linear.weight'],
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)
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def setup_dist_env(self):
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fleet.init(is_collective=True)
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def run_save_state_dict(self):
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self.setup_dist_env()
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model = SimpleMLP()
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sharded_state_dict = model.sharded_state_dict()
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dist.save_state_dict(sharded_state_dict, self.ckpt_path)
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if __name__ == '__main__':
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TestLoadStateDictCastLogic().run_test()
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