Making large AI models cheaper, faster and more accessible
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#!/usr/bin/env python
import torch
import torch.nn as nn
from colossalai.nn import CheckpointModule
from .registry import non_distributed_component_funcs
from .utils.dummy_data_generator import DummyDataGenerator
class NetWithRepeatedlyComputedLayers(CheckpointModule):
"""
This model is to test with layers which go through forward pass multiple times.
In this model, the fc1 and fc2 call forward twice
"""
def __init__(self, checkpoint=False) -> None:
super().__init__(checkpoint=checkpoint)
self.fc1 = nn.Linear(5, 5)
self.fc2 = nn.Linear(5, 5)
self.fc3 = nn.Linear(5, 2)
self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
class DummyDataLoader(DummyDataGenerator):
def generate(self):
data = torch.rand(16, 5)
label = torch.randint(low=0, high=2, size=(16,))
return data, label
@non_distributed_component_funcs.register(name='repeated_computed_layers')
def get_training_components():
def model_builder(checkpoint=False):
return NetWithRepeatedlyComputedLayers(checkpoint)
trainloader = DummyDataLoader()
testloader = DummyDataLoader()
criterion = torch.nn.CrossEntropyLoss()
return model_builder, trainloader, testloader, torch.optim.Adam, criterion