github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-22 18:09:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

[test] merge old components to test to model zoo (#4945)

Browse Source 
* [test] add custom models in model zoo

* [test] update legacy test

* [test] update model zoo

* [test] update gemini test

* [test] remove components to test
This commit is contained in:
Hongxin Liu
2023-10-20 10:35:08 +08:00
committed by GitHub
parent 3a41e8304e
commit b8e770c832
49 changed files with 461 additions and 914 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
tests/test_legacy/test_engine/test_engine.py
View File

@@ -1,10 +1,11 @@
import pytest
import torch
import colossalai
from colossalai.legacy.amp import AMP_TYPE
from colossalai.legacy.core import global_context as gpc
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from tests.components_to_test.registry import non_distributed_component_funcs
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
CONFIG = dict(
parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)), fp16=dict(mode=None), clip_grad_norm=1.0
@@ -15,29 +16,29 @@ CONFIG = dict(
@parameterize("amp_mode", [AMP_TYPE.APEX, AMP_TYPE.TORCH, AMP_TYPE.NAIVE, None])
def run_train(model_name, amp_mode):
# FIXME: test bert
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
train_dataloader = DummyDataloader(data_gen_fn)
criterion = lambda x: x.sum()
gpc.config.fp16["mode"] = amp_mode
model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
model = model_builder(checkpoint=False)
model = model_builder()
engine, train_dataloader, *args = colossalai.legacy.initialize(
model=model,
optimizer=optimizer_class(model.parameters(), lr=1e-3),
optimizer=torch.optim.Adam(model.parameters(), lr=1e-3),
criterion=criterion,
train_dataloader=train_dataloader,
)
try:
engine.train()
for data, label in train_dataloader:
for data in train_dataloader:
engine.zero_grad()
data = data.cuda()
label = label.cuda()
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
if criterion:
output = engine(data)
loss = engine.criterion(output, label)
output = engine(**data)
loss = engine.criterion(output)
else:
loss = engine(data, label)
loss = engine(**data)
engine.backward(loss)
engine.step()
break