mirror of https://github.com/hpcaitech/ColossalAI
200 lines
8.2 KiB
Python
200 lines
8.2 KiB
Python
from copy import copy
|
|
from colossalai.utils.cuda import get_current_device
|
|
from colossalai.utils.model.colo_init_context import ColoInitContext
|
|
import torch
|
|
from colossalai.context.parallel_mode import ParallelMode
|
|
from colossalai.tensor import ColoTensor, distspec
|
|
|
|
from functools import partial
|
|
|
|
import colossalai
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
import torch.nn.functional as F
|
|
from colossalai.testing import rerun_if_address_is_in_use
|
|
from colossalai.utils import free_port
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager, register_colo_module, init_colo_module, check_colo_module
|
|
from _utils import tensor_equal, tensor_shard_equal, set_seed
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
|
|
def run_model_with_spec(mode, model_name):
|
|
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
|
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
|
rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
|
|
|
|
set_seed(1)
|
|
with ColoInitContext(device=get_current_device()):
|
|
model = model_builder(checkpoint=False)
|
|
|
|
if rank == 0:
|
|
model_seq = model_builder(checkpoint=False)
|
|
model_seq = model_seq.cuda()
|
|
|
|
# Make two models have the same init params
|
|
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
|
|
p2.data.copy_(p1.data)
|
|
|
|
parallel_action = ParallelAction(ComputePattern.TP1D)
|
|
# Not all layers in Bert can be mod by 4.
|
|
# e.g. row shard for all layers is invalid because the first dim of some layer is the classification type size 2.
|
|
if 'bert' == model_name:
|
|
if 'col' == mode:
|
|
init_colo_module(model.bert.embeddings, parallel_action, recursive=True, mode=mode)
|
|
init_colo_module(model.bert.encoder, parallel_action, recursive=True, mode=mode)
|
|
init_colo_module(model.classifier, parallel_action, recursive=True, mode='row')
|
|
elif 'row' == mode:
|
|
init_colo_module(model.bert.embeddings, parallel_action, recursive=True, mode='col')
|
|
init_colo_module(model.bert.encoder, parallel_action, recursive=True, mode=mode)
|
|
init_colo_module(model.classifier, parallel_action, recursive=True, mode=mode)
|
|
elif 'simple_net' == model_name:
|
|
init_colo_module(model, parallel_action, recursive=True, mode=mode)
|
|
|
|
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=gpc.get_group(ParallelMode.PARALLEL_1D))
|
|
torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
|
|
|
|
if criterion:
|
|
output = model(data)
|
|
loss = criterion(output, label)
|
|
else:
|
|
output = model(data, label)
|
|
loss = output
|
|
|
|
# For reference
|
|
if rank == 0:
|
|
if criterion:
|
|
output_seq = model_seq(data)
|
|
loss_seq = criterion(output_seq, label)
|
|
else:
|
|
output_seq = model_seq(data, label)
|
|
loss_seq = output_seq
|
|
|
|
if rank == 0:
|
|
with torch.no_grad():
|
|
assert torch.allclose(loss, loss_seq, rtol=1e-2)
|
|
|
|
loss.backward()
|
|
|
|
if rank == 0:
|
|
loss_seq.backward()
|
|
|
|
with torch.no_grad():
|
|
# check param
|
|
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
|
|
if p1.size() == p2.size():
|
|
assert torch.allclose(p1, p2)
|
|
else:
|
|
if p1.size(-1) < p2.size(-1): # col
|
|
world_size = p2.size(-1) // p1.size(-1)
|
|
split_p2 = torch.chunk(p2, world_size, dim=-1)[0]
|
|
|
|
elif p1.size(0) < p2.size(0): # row
|
|
world_size = p2.size(0) // p1.size(0)
|
|
split_p2 = torch.chunk(p2, world_size, dim=0)[0]
|
|
|
|
assert torch.allclose(p1, split_p2)
|
|
|
|
if i > 3:
|
|
break
|
|
|
|
def run_linear_with_spec(mode):
|
|
with ColoInitContext(device=get_current_device()):
|
|
model = torch.nn.Linear(4, 8)
|
|
|
|
model_handy = copy(model)
|
|
|
|
parallel_action = ParallelAction(ComputePattern.TP1D)
|
|
init_colo_module(model, parallel_action, recursive=True, mode=mode)
|
|
|
|
x = torch.rand(2, 4).cuda()
|
|
out = model(x)
|
|
colo_out = model_handy(x)
|
|
assert tensor_equal(out, colo_out)
|
|
grad = torch.rand_like(out)
|
|
out.backward(grad)
|
|
colo_out.backward(grad)
|
|
assert tensor_shard_equal(model.weight.grad, model_handy.weight.grad)
|
|
assert tensor_shard_equal(model.bias.grad, model_handy.bias.grad)
|
|
|
|
def run_check_shared_param():
|
|
from transformers import BertForMaskedLM, BertConfig
|
|
hidden_dim = 8
|
|
num_head = 4
|
|
sequence_length = 12
|
|
num_layer = 2
|
|
vocab_size = 24
|
|
|
|
config = BertConfig(vocab_size=vocab_size,
|
|
hidden_size=hidden_dim,
|
|
intermediate_size=hidden_dim * 4,
|
|
num_attention_heads=num_head,
|
|
max_position_embeddings=sequence_length,
|
|
num_hidden_layers=num_layer,
|
|
hidden_dropout_prob=0.,
|
|
attention_probs_dropout_prob=0.)
|
|
with ColoInitContext(lazy_memory_allocate=False, device=get_current_device()):
|
|
model = BertForMaskedLM(config)
|
|
|
|
model = model.cuda()
|
|
parallel_action = ParallelAction(ComputePattern.TP1D)
|
|
# model.cls.predictions.decoder and model.cls.predictions share the bias, so they should have the same spec
|
|
assert len(model.cls.predictions.decoder.bias.shared_param_modules) == 2
|
|
# They are all Linear, so both row is allowed. This should pass check.
|
|
init_colo_module(model, parallel_action, recursive=True, mode='row')
|
|
# This should be detected by check because you can not set weight as row while set bias as col.
|
|
col_spec = TensorSpec(
|
|
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
|
|
ParallelAction(ComputePattern.TP1D))
|
|
model.cls.predictions.bias.set_spec(col_spec)
|
|
try:
|
|
check_colo_module(model.cls.predictions.decoder, recursive=False)
|
|
except Exception as e:
|
|
assert 'incorrectly sharded' in str(e)
|
|
|
|
def run_dist(rank, world_size, port):
|
|
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
run_linear_with_spec('col')
|
|
run_linear_with_spec('row')
|
|
|
|
def run_dist_model(rank, world_size, port):
|
|
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
for model_name in ['simple_net', 'bert']:
|
|
run_model_with_spec('col', model_name)
|
|
run_model_with_spec('row', model_name)
|
|
|
|
def run_dist_check(rank, world_size, port):
|
|
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
run_check_shared_param()
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_module_linear_1d(world_size):
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_module_model(world_size):
|
|
run_func = partial(run_dist_model, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_module_check(world_size):
|
|
run_func = partial(run_dist_check, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
if __name__ == '__main__':
|
|
test_module_check(2) |