wehub-resource-sync
98 lines
3.0 KiB
Python
98 lines
3.0 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 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.distributed import Replicate, Shard
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from paddle.io import DataLoader
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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(2024)
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paddle.seed(2024)
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, images, labels, num_samples):
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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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def __getitem__(self, idx):
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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 DemoNet(nn.Layer):
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def __init__(self, mesh, shard=True):
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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.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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self.shard = shard
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# shard the weights of this layer
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if self.shard:
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self.linear_0.weight = dist.shard_tensor(
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self.linear_0.weight,
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self._mesh,
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[Shard(1)],
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stop_gradient=False,
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)
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self.linear_1.weight = dist.shard_tensor(
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self.linear_1.weight,
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self._mesh,
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[Shard(0)],
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stop_gradient=False,
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)
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else:
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self.linear_0.weight = dist.shard_tensor(
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self.linear_0.weight,
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self._mesh,
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[Replicate()],
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stop_gradient=False,
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)
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self.linear_1.weight = dist.shard_tensor(
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self.linear_1.weight,
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self._mesh,
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[Replicate()],
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stop_gradient=False,
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)
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def forward(self, x):
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x.stop_gradient = False
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out = self.relu_0(x) # triggle backward partial allreduce
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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) # triggle forward partial allreduce
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return out
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def create_data_loader():
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images = np.random.rand(BATCH_NUM, IMAGE_SIZE).astype('float32')
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labels = np.random.rand(BATCH_NUM, CLASS_NUM).astype('float32')
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dataset = RandomDataset(images, labels, BATCH_NUM)
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loader = DataLoader(dataset, batch_size=BATCH_SIZE)
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return loader
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