mirror of https://github.com/hpcaitech/ColossalAI
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import torch
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import torch.nn as nn
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from colossalai.nn import CheckpointModule
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from .utils.dummy_data_generator import DummyDataGenerator
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from .registry import non_distributed_component_funcs
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from colossalai.utils.cuda import get_current_device
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class SimpleNet(CheckpointModule):
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"""
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In this no-leaf module, it has subordinate nn.modules and a nn.Parameter.
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"""
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def __init__(self, checkpoint=False) -> None:
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super().__init__(checkpoint=checkpoint)
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self.embed = nn.Embedding(20, 4)
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self.proj1 = nn.Linear(4, 8)
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self.ln1 = nn.LayerNorm(8)
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self.proj2 = nn.Linear(8, 4)
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self.ln2 = nn.LayerNorm(4)
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self.classifier = nn.Linear(4, 4)
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def forward(self, x):
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x = self.embed(x)
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x = self.proj1(x)
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x = self.ln1(x)
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x = self.proj2(x)
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x = self.ln2(x)
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x = self.classifier(x)
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return x
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class DummyDataLoader(DummyDataGenerator):
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def generate(self):
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data = torch.randint(low=0, high=20, size=(16,), device=get_current_device())
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label = torch.randint(low=0, high=2, size=(16,), device=get_current_device())
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return data, label
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@non_distributed_component_funcs.register(name='simple_net')
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def get_training_components():
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def model_builder(checkpoint=True):
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return SimpleNet(checkpoint)
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = torch.nn.CrossEntropyLoss()
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from colossalai.nn.optimizer import HybridAdam
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return model_builder, trainloader, testloader, HybridAdam, criterion
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