mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
49 lines
1.4 KiB
49 lines
1.4 KiB
#!/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
|
|
|