wehub-resource-sync
69 lines
2.1 KiB
Python
69 lines
2.1 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 unittest
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import numpy as np
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from test_to_static_pir_program import (
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DemoNet,
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create_data_loader,
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)
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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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BATCH_SIZE = 4
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BATCH_NUM = 40
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IMAGE_SIZE = 16
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CLASS_NUM = 8
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np.random.seed(2025)
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paddle.seed(2025)
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class LocalLossLayer(dist.LocalLayer):
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def __init__(self, out_dist_attrs, grad_dist_attrs):
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super().__init__(
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out_dist_attrs=out_dist_attrs, grad_dist_attrs=grad_dist_attrs
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)
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self.loss = nn.MSELoss()
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def forward(self, input, label):
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return self.loss(input, label)
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class TestMLPTensorParallel(unittest.TestCase):
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def test_to_static_program(self):
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mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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mp_layer = DemoNet(mesh, shard=False)
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opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=mp_layer.parameters()
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)
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placement = [dist.Partial(dist.ReduceType.kRedSum)]
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out_dist_attrs = [(mesh, placement)]
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grad_dist_attrs = [(mesh, placement), None] # input, label
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loss_fn = LocalLossLayer(out_dist_attrs, grad_dist_attrs)
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loader = create_data_loader()
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dist_loader = dist.shard_dataloader(loader, meshes=[mesh])
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dist_model = dist.to_static(mp_layer, dist_loader, loss_fn, opt)
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dist_model.train()
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for batch_id, (image, label) in enumerate(dist_loader()):
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loss = dist_model(image, label)
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if __name__ == "__main__":
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unittest.main()
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