ColossalAI/tests/test_tensor/model/test_model.py

341 lines
11 KiB
Python

from functools import partial
import pytest
import torch
import torch.multiprocessing as mp
import colossalai
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.tensor import ColoTensor, ProcessGroup
from colossalai.tensor.colo_parameter import ColoParameter
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.utils.cuda import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import (
check_equal,
set_seed,
split_param_col_tp1d,
split_param_row_tp1d,
tensor_shard_equal,
)
def run_1d_hybrid_tp(model_name):
# A simple net with two stacked nn.Linear
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
optimizer_torch = ColossalaiOptimizer(torch.optim.SGD(model_torch.parameters(), lr=0.1))
# Make two models have the same init params
for p1, p2 in zip(model.parameters(), model_torch.parameters()):
p2.data.copy_(p1.data)
else:
model_torch = None
optimizer_torch = None
pg = ProcessGroup(tp_degree=world_size)
if 'bert' == model_name:
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
# num_class = type_vocab_size = 2 | (8, 2)
if 'classifier' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
# num_class = vocab_size = 30524 | (30524, 8)
elif 'word_embeddings' in name and 'weight' in name:
split_param_row_tp1d(p, pg)
# num_class = seq_len = 512 | (512, 8)
elif 'position_embeddings' in name and 'weight' in name:
split_param_row_tp1d(p, pg)
# num_class = type_vocab_size = 2 | (2, 8)
elif 'token_type_embeddings' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
elif "simple_net" == model_name:
# A naive way to set spec for all weights in Linear
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
if 'embed' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
if 'proj1' in name and ('weight' in name or 'bias' in name):
split_param_row_tp1d(p, pg)
if 'proj2' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
if 'classifier' in name and ('weight' in name or 'bias' in name):
split_param_row_tp1d(p, pg)
model = model.cuda()
model.eval()
if rank == 0:
model_torch.eval()
colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
for i, (data, label) in enumerate(train_dataloader):
# Zero grad
colo_optimizer.zero_grad()
if rank == 0:
optimizer_torch.zero_grad()
torch.distributed.barrier()
data = data.to(get_current_device())
label = label.to(get_current_device())
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
# Test output
if rank == 0:
if criterion:
output_torch = model_torch(data)
loss_torch = criterion(output_torch, label)
else:
output_torch = model_torch(data, label)
loss_torch = output_torch
assert torch.allclose(loss, loss_torch, rtol=1e-2)
torch.distributed.barrier()
loss.backward()
colo_optimizer.step()
if rank == 0:
loss_torch.backward()
optimizer_torch.step()
with torch.no_grad():
# check param
for p, torch_p in zip(model.parameters(), model_torch.parameters()):
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
torch.distributed.barrier()
if i > 5:
break
# Test the overrided parameters() and named_parameters() member functions
def test_model_parameters():
colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl')
# build a module with 2 Linear, 4 parameters in total.
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.fcs = torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 2))
self.extra_param = torch.nn.Parameter(torch.randn(2))
with ColoInitContext(device=get_current_device()):
model = Net()
param_cnt = 0
for name, p in model.named_parameters():
param_cnt += 1
assert param_cnt == 5
for name, colo_p in model.named_parameters():
assert colo_p.is_model_data()
param_cnt = 0
for name, p in model.named_parameters(recurse=False):
param_cnt += 1
assert param_cnt == 1
param_cnt = 0
for p in model.fcs[0].parameters(recurse=False):
param_cnt += 1
assert param_cnt == 2
def test_colo_optimizer():
get_components_func = non_distributed_component_funcs.get_callable('simple_net')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
for i, (data, label) in enumerate(train_dataloader):
colo_optimizer.zero_grad()
data = data.to(get_current_device())
label = label.to(get_current_device())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
loss.backward()
colo_optimizer.step()
if i > 5:
break
def run_1d_row_tp(model_name: str):
# A simple net with two stacked nn.Linear
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = torch.distributed.get_rank()
set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
set_seed(1)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
# A naive way to set spec for all weights in Linear
for mo_name, module in model.named_modules():
# print(mo_name)
for pa_name, param in module.named_parameters(recurse=False):
# print('\t', pa_name, param.shape)
if not isinstance(param, ColoTensor):
continue
if 'weight' in pa_name:
if 'embed' in mo_name and 'token' not in mo_name and 'LayerNorm' not in mo_name:
split_param_row_tp1d(param, pg)
elif 'LayerNorm' not in mo_name and 'ln' not in mo_name:
split_param_col_tp1d(param, pg)
model = model.cuda()
for i, (data, label) in enumerate(train_dataloader):
data = data.to(get_current_device())
label = label.to(get_current_device())
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
# For reference
if rank == 0:
if criterion:
output_torch = model_torch(data)
loss_torch = criterion(output_torch, label)
else:
output_torch = model_torch(data, label)
loss_torch = output_torch
assert torch.allclose(loss, loss_torch, rtol=1e-2)
torch.distributed.barrier()
loss.backward()
if rank == 0:
loss_torch.backward()
torch.distributed.barrier()
if i > 5:
break
def _run_pretrain_load():
from transformers import BertForMaskedLM
set_seed(1)
model_pretrained = BertForMaskedLM.from_pretrained('bert-base-uncased')
with ColoInitContext(device=get_current_device()):
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model_pretrained = model_pretrained.cuda()
model = model.cuda()
dict_pretrained = {}
dict_col = {}
c_ref = 0
for name, param in model_pretrained.named_parameters():
dict_pretrained[name] = param
c_ref += 1
c1 = 0
c2 = 0
for name, param in model.named_parameters():
if isinstance(param, ColoParameter):
c1 += 1
else:
c2 += 1
dict_col[name] = param
assert c_ref == c1
assert c2 == 0
if model_pretrained.cls.predictions.decoder.bias is model_pretrained.cls.predictions.bias:
assert model.cls.predictions.decoder.bias is model.cls.predictions.bias
for name, param in dict_pretrained.items():
check_equal(param, dict_col[name])
def run_model_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# Comment below test for speed consideration
# for name in ['bert', 'simple_net']:
# run_1d_row_tp(name)
for name in ['bert', 'simple_net']:
run_1d_hybrid_tp(name)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_model(world_size):
run_func = partial(run_model_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
def run_pretrain_load_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
_run_pretrain_load()
# The test case has to download huggingface pretrained models from the internet
# So we manually trigger the test.
@pytest.mark.skip
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_pretrain_load(world_size):
run_func = partial(run_pretrain_load_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
# test_model_parameters()
# test_colo_optgimizer()
test_model(4)
# test_pretrain_load(4)