wehub-resource-sync
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
# Copyright (c) 2022 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 unittest
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from dygraph_to_static_utils import Dy2StTestBase
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import paddle
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import paddle.distributed as dist
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from paddle import nn
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class Net(nn.Layer):
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def __init__(self):
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super().__init__()
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self.emb1 = nn.Embedding(100, 16)
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self.emb2 = nn.Embedding(100, 16)
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def forward(self, ids):
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feat1 = self.emb1(ids)
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feat1.stop_gradient = True # here
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feat2 = self.emb2(ids)
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out = feat1 + feat2
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out = paddle.mean(out)
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return out
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def train():
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paddle.distributed.init_parallel_env()
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net = paddle.jit.to_static(Net())
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sgd = paddle.optimizer.SGD(learning_rate=0.1, parameters=net.parameters())
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dp_net = paddle.DataParallel(net)
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for i in range(4):
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x = paddle.randint(low=0, high=100, shape=[4, 10])
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loss = dp_net(x)
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loss.backward()
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sgd.step()
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loss.clear_gradient()
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print(loss)
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class TestParamsNoGrad(Dy2StTestBase):
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def test_two_card(self):
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if (
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paddle.is_compiled_with_cuda()
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and len(paddle.static.cuda_places()) > 1
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):
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dist.spawn(train, nprocs=2, gpus='0,1')
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if __name__ == '__main__':
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unittest.main()
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