wehub-resource-sync
146 lines
4.5 KiB
Python
146 lines
4.5 KiB
Python
# Copyright (c) 2024 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 random
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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 import nn
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from paddle.io import DataLoader
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BATCH_SIZE = 4
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BATCH_NUM = 4
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IMAGE_SIZE = 16
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CLASS_NUM = 8
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, images, labels, num_samples, return_dict=False):
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self.images = images
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self.labels = labels
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self.num_samples = num_samples
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self.return_dict = return_dict
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def __getitem__(self, idx):
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if self.return_dict:
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return {
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"image": self.images[idx],
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"label": self.labels[idx],
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}
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else:
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return self.images[idx], self.labels[idx]
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def __len__(self):
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return self.num_samples
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class DPDemoNet(nn.Layer):
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def __init__(
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self,
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mesh,
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):
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super().__init__()
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self._mesh = mesh
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self.linear_0 = nn.Linear(IMAGE_SIZE, IMAGE_SIZE, bias_attr=False)
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self.linear_1 = nn.Linear(IMAGE_SIZE, CLASS_NUM, bias_attr=False)
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self.linear_0.weight = paddle.distributed.shard_tensor(
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self.linear_0.weight,
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self._mesh,
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[paddle.distributed.Replicate()],
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stop_gradient=False,
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)
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self.linear_1.weight = paddle.distributed.shard_tensor(
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self.linear_1.weight,
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self._mesh,
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[paddle.distributed.Replicate()],
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stop_gradient=False,
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)
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self.relu_0 = nn.ReLU()
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self.relu_1 = nn.ReLU()
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self.relu_2 = nn.ReLU()
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def forward(self, x):
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out = self.relu_0(x)
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out = self.linear_0(out)
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out = self.relu_1(out)
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out = self.linear_1(out)
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out = self.relu_2(out)
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out = paddle.cast(out, 'float32')
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return out
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class TestSimpleNetForSemiAutoParallelSOT:
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def __init__(self):
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self._seed = eval(os.getenv("seed"))
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self.mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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self._in_pir_mode = paddle.base.framework.get_flags(
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"FLAGS_enable_pir_api"
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)["FLAGS_enable_pir_api"]
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def set_random_seed(self, seed):
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random.seed(seed)
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np.random.seed(seed)
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paddle.seed(seed)
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def create_data_loader(self, return_dict=False):
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images = np.random.rand(BATCH_SIZE, IMAGE_SIZE).astype('float32')
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labels = np.random.rand(BATCH_SIZE, CLASS_NUM).astype('float32')
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dataset = RandomDataset(images, labels, BATCH_SIZE, return_dict)
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loader = DataLoader(dataset, batch_size=BATCH_SIZE)
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return loader
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def run_dynamic(self, layer, opt, dist_loader):
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# create loss
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loss_fn = nn.MSELoss()
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loss_list = []
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for epoch in range(BATCH_NUM):
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for batch_id, data in enumerate(dist_loader()):
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if isinstance(data, dict):
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image = data['image']
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label = data['label']
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else:
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image, label = data
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out = layer(image)
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loss = loss_fn(out, label)
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loss_list.append(loss.numpy())
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loss.backward()
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opt.step()
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opt.clear_grad()
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return np.array(loss_list)
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def test_dp_demo_net(self):
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paddle.disable_static()
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self.set_random_seed(self._seed)
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data_loader = self.create_data_loader()
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dist_dataloader = dist.shard_dataloader(
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dataloader=data_loader, meshes=[self.mesh]
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)
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dy_layer = DPDemoNet(self.mesh)
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unified_strategy = dist.Strategy({"full_graph": False})
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dy_layer = dist.to_static(dy_layer, strategy=unified_strategy)
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dy_opt = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=dy_layer.parameters()
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)
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dy_losses = self.run_dynamic(dy_layer, dy_opt, dist_dataloader)
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def run_test_case(self):
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self.test_dp_demo_net()
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if __name__ == '__main__':
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TestSimpleNetForSemiAutoParallelSOT().run_test_case()
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