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[AutoParallel] Verify auto parallel in amp mode (#58172)
* add amp test * support apm o1 mode * add share_data_with method for dist tensor
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test/auto_parallel/semi_auto_parallel_simple_net_amp.py
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| # Copyright (c) 2023 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. | ||
|
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| import os | ||
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||
| from semi_auto_parallel_simple_net import ( | ||
| DemoNet, | ||
| DPDemoNet, | ||
| MPDemoNet, | ||
| TestSimpleNetForSemiAutoParallel, | ||
| ) | ||
|
|
||
| import paddle | ||
| import paddle.distributed as dist | ||
| from paddle import nn | ||
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| class TestSimpleNetWithAmpForSemiAutoParallel(TestSimpleNetForSemiAutoParallel): | ||
| def __init__(self): | ||
| self._dtype = os.getenv("dtype") | ||
| self._backend = os.getenv("backend") | ||
| self._seed = eval(os.getenv("seed")) | ||
| self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"]) | ||
|
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| paddle.set_device(self._backend) | ||
| self.init_input_data() | ||
| self.init_single_card_net_result() | ||
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| def run_dynamic_amp(self, layer, level='O1'): | ||
| if level == 'O2': | ||
| layer = paddle.amp.decorate(models=layer, level='O2') | ||
| # create loss | ||
| loss_fn = nn.MSELoss() | ||
| scaler = paddle.amp.GradScaler(init_loss_scaling=1024) | ||
| # run forward and backward | ||
| image = paddle.to_tensor(self.image) | ||
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| with paddle.amp.auto_cast(level=level): | ||
| out = layer(image) | ||
| label = paddle.to_tensor(self.label) | ||
| loss = loss_fn(out, label) | ||
|
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| scaled = scaler.scale(loss) | ||
| scaled.backward() | ||
| return loss, layer.w0.grad, layer.w1.grad | ||
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| def init_single_card_net_result(self): | ||
| ( | ||
| self.base_loss_o1, | ||
| self.base_w0_grad_o1, | ||
| self.base_w1_grad_o1, | ||
| ) = self.run_dynamic_amp(DemoNet(self.w0, self.w1, 'O1'), 'O1') | ||
| ( | ||
| self.base_loss_o2, | ||
| self.base_w0_grad_o2, | ||
| self.base_w1_grad_o2, | ||
| ) = self.run_dynamic_amp(DemoNet(self.w0, self.w1, 'O2'), 'O2') | ||
|
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| def test_dp_demo_net(self): | ||
| ( | ||
| self.dp_loss_o1, | ||
| self.dp_w0_grad_o1, | ||
| self.dp_w1_grad_o1, | ||
| ) = self.run_dynamic_amp( | ||
| DPDemoNet(self.w0, self.w1, self._mesh, 'O1'), 'O1' | ||
| ) | ||
| self.check_tensor_eq(self.dp_loss_o1, self.base_loss_o1) | ||
| self.check_tensor_eq(self.dp_w0_grad_o1, self.base_w0_grad_o1) | ||
| self.check_tensor_eq(self.dp_w1_grad_o1, self.base_w1_grad_o1) | ||
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| ( | ||
| self.dp_loss_o2, | ||
| self.dp_w0_grad_o2, | ||
| self.dp_w1_grad_o2, | ||
| ) = self.run_dynamic_amp( | ||
| DPDemoNet(self.w0, self.w1, self._mesh, 'O2'), 'O2' | ||
| ) | ||
| self.check_tensor_eq(self.dp_loss_o2, self.base_loss_o2) | ||
| self.check_tensor_eq(self.dp_w0_grad_o2, self.base_w0_grad_o2) | ||
| self.check_tensor_eq(self.dp_w1_grad_o2, self.base_w1_grad_o2) | ||
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| def test_mp_demo_net(self): | ||
| ( | ||
| self.mp_loss_o1, | ||
| self.mp_w0_grad_o1, | ||
| self.mp_w1_grad_o1, | ||
| ) = self.run_dynamic_amp( | ||
| MPDemoNet(self.w0, self.w1, self._mesh, 'O1'), 'O1' | ||
| ) | ||
| self.check_tensor_eq(self.mp_loss_o1, self.base_loss_o1) | ||
| self.check_tensor_eq(self.mp_w0_grad_o1, self.base_w0_grad_o1) | ||
| self.check_tensor_eq(self.mp_w1_grad_o1, self.base_w1_grad_o1) | ||
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| ( | ||
| self.mp_loss_o2, | ||
| self.mp_w0_grad_o2, | ||
| self.mp_w1_grad_o2, | ||
| ) = self.run_dynamic_amp( | ||
| MPDemoNet(self.w0, self.w1, self._mesh, 'O2'), 'O2' | ||
| ) | ||
| self.check_tensor_eq(self.mp_loss_o2, self.base_loss_o2) | ||
| self.check_tensor_eq(self.mp_w0_grad_o2, self.base_w0_grad_o2) | ||
| self.check_tensor_eq(self.mp_w1_grad_o2, self.base_w1_grad_o2) | ||
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| def run_test_case(self): | ||
| self.test_dp_demo_net() | ||
| self.test_mp_demo_net() | ||
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| if __name__ == '__main__': | ||
| TestSimpleNetWithAmpForSemiAutoParallel().run_test_case() |
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