mirror of https://github.com/hpcaitech/ColossalAI
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.
172 lines
6.9 KiB
172 lines
6.9 KiB
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.optim import Adam
|
|
from utils import shared_tempdir
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin import GeminiPlugin, TorchDDPPlugin
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.testing import (
|
|
check_state_dict_equal,
|
|
clear_cache_before_run,
|
|
parameterize,
|
|
rerun_if_address_is_in_use,
|
|
spawn,
|
|
)
|
|
from tests.kit.model_zoo import model_zoo
|
|
|
|
|
|
@clear_cache_before_run()
|
|
@parameterize('shard', [False, True])
|
|
@parameterize('model_name', ['transformers_gpt'])
|
|
def exam_torch_load_from_gemini(shard: bool, model_name: str):
|
|
|
|
(model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
|
criterion = lambda x: x.mean()
|
|
plugin = GeminiPlugin(precision="fp16", initial_scale=(2**14))
|
|
booster = Booster(plugin=plugin)
|
|
|
|
model = model_fn()
|
|
optimizer = HybridAdam(model.parameters(), lr=0.001)
|
|
model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
|
|
|
|
data = data_gen_fn()
|
|
data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()}
|
|
output = model(**data)
|
|
output = output_transform_fn(output)
|
|
output_key = list(output.keys())[0]
|
|
loss = criterion(output[output_key])
|
|
|
|
booster.backward(loss, optimizer)
|
|
optimizer.step()
|
|
|
|
with shared_tempdir() as tempdir:
|
|
model_ckpt_path = f"{tempdir}/model"
|
|
optimizer_ckpt_path = f"{tempdir}/optimizer"
|
|
|
|
booster.save_model(model, model_ckpt_path, shard=shard)
|
|
booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
|
|
dist.barrier()
|
|
|
|
new_model = model_fn()
|
|
new_optimizer = Adam(new_model.parameters(), lr=0.001)
|
|
new_plugin = TorchDDPPlugin()
|
|
new_booster = Booster(plugin=new_plugin)
|
|
new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
|
|
|
|
# Loading HybridAdam states to torch.Adam
|
|
new_booster.load_model(new_model, model_ckpt_path, strict=True)
|
|
|
|
# Add prefix to get aligned with pytorch parameter names.
|
|
check_state_dict_equal(
|
|
model.unwrap().state_dict(only_rank_0=False, prefix='module.module.', dtype=torch.float32),
|
|
new_model.state_dict(), False)
|
|
|
|
new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
|
|
check_state_dict_equal(optimizer.unwrap().state_dict(only_rank_0=False), new_optimizer.state_dict(), False)
|
|
|
|
# Check the new model/optimizer can successfully run.
|
|
data = data_gen_fn()
|
|
data = {
|
|
k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
|
|
}
|
|
output = new_model(**data)
|
|
output = output_transform_fn(output)
|
|
output_key = list(output.keys())[0]
|
|
loss = criterion(output[output_key])
|
|
new_booster.backward(loss, new_optimizer)
|
|
new_optimizer.step()
|
|
new_booster.save_model(new_model, model_ckpt_path, shard=shard)
|
|
new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
|
|
|
|
|
|
@clear_cache_before_run()
|
|
@parameterize('shard', [False, True])
|
|
@parameterize('model_name', ['transformers_gpt'])
|
|
def exam_gemini_load_from_torch(shard: bool, model_name: str):
|
|
|
|
(model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
|
criterion = lambda x: x.mean()
|
|
plugin = TorchDDPPlugin()
|
|
booster = Booster(plugin=plugin)
|
|
|
|
model = model_fn()
|
|
optimizer = Adam(model.parameters(), lr=0.001)
|
|
model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
|
|
|
|
data = data_gen_fn()
|
|
data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()}
|
|
output = model(**data)
|
|
output = output_transform_fn(output)
|
|
output_key = list(output.keys())[0]
|
|
loss = criterion(output[output_key])
|
|
|
|
booster.backward(loss, optimizer)
|
|
optimizer.step()
|
|
|
|
with shared_tempdir() as tempdir:
|
|
model_ckpt_path = f"{tempdir}/model"
|
|
optimizer_ckpt_path = f"{tempdir}/optimizer"
|
|
|
|
booster.save_model(model, model_ckpt_path, shard=shard)
|
|
booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
|
|
dist.barrier()
|
|
|
|
new_model = model_fn()
|
|
new_optimizer = HybridAdam(new_model.parameters(), lr=0.001)
|
|
new_plugin = GeminiPlugin()
|
|
new_booster = Booster(plugin=new_plugin)
|
|
new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
|
|
|
|
# Loading torch.Adam states to HybridAdam
|
|
new_booster.load_model(new_model, model_ckpt_path, strict=True)
|
|
|
|
# Add prefix to get aligned with pytorch parameter names.
|
|
check_state_dict_equal(
|
|
new_model.unwrap().state_dict(only_rank_0=False, prefix='module.module.', dtype=torch.float32),
|
|
model.state_dict(), False)
|
|
|
|
new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
|
|
old_state_dict = optimizer.state_dict()
|
|
new_state_dict = new_optimizer.unwrap().state_dict(only_rank_0=False)
|
|
|
|
# Comparison of param_groups needs special care here,
|
|
# since not all hyperparameters in Adam are used by HybridAdam
|
|
hyperparameters_to_examine = ['params', 'lr', 'betas', 'eps', 'weight_decay']
|
|
for old_group, new_group in zip(old_state_dict['param_groups'], new_state_dict['param_groups']):
|
|
for k in hyperparameters_to_examine:
|
|
assert k in old_group and k in new_group, \
|
|
f"Old group's keys: {list(old_group.keys())}, New group's keys: {list(new_group.keys())}"
|
|
assert old_group[k] == new_group[k]
|
|
check_state_dict_equal(old_state_dict['state'], new_state_dict['state'], False)
|
|
|
|
# Check the new model/optimizer can successfully run.
|
|
data = data_gen_fn()
|
|
data = {
|
|
k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
|
|
}
|
|
output = new_model(**data)
|
|
output = output_transform_fn(output)
|
|
output_key = list(output.keys())[0]
|
|
loss = criterion(output[output_key])
|
|
new_booster.backward(loss, new_optimizer)
|
|
new_optimizer.step()
|
|
new_booster.save_model(new_model, model_ckpt_path, shard=shard)
|
|
new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
config = {}
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
exam_torch_load_from_gemini()
|
|
exam_gemini_load_from_torch()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_gemini_ckpIO(world_size):
|
|
spawn(run_dist, world_size)
|