mirror of https://github.com/hpcaitech/ColossalAI
154 lines
5.6 KiB
Python
154 lines
5.6 KiB
Python
from functools import partial
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
|
|
import colossalai
|
|
from colossalai.nn.parallel.data_parallel import ColoDDP
|
|
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
|
|
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 (
|
|
debug_print,
|
|
set_seed,
|
|
split_param_col_tp1d,
|
|
split_param_row_tp1d,
|
|
tensor_equal,
|
|
tensor_shard_equal,
|
|
)
|
|
|
|
|
|
def init_1d_row_spec(model, pg: ProcessGroup):
|
|
tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
|
|
for n, p in model.named_parameters():
|
|
p.set_process_group(pg)
|
|
if 'weight' in n and 'ln' not in n:
|
|
p.set_tensor_spec(*tensor_spec)
|
|
|
|
|
|
def init_1d_col_spec(model, pg: ProcessGroup):
|
|
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
|
|
|
|
for n, p in model.named_parameters():
|
|
p.set_process_group(pg)
|
|
if 'ln' not in n and ('weight' in n or 'bias' in n):
|
|
p.set_tensor_spec(*spec)
|
|
|
|
|
|
def init_megatron_spec(model, pg: ProcessGroup):
|
|
for mn, module in model.named_modules():
|
|
# debug_print([0], mn)
|
|
for pn, param in module.named_parameters(recurse=False):
|
|
# debug_print([0], '\t', pn, param.compute_spec, param.shape)
|
|
param.set_process_group(pg)
|
|
|
|
if 'mlp.c_fc' in mn:
|
|
if 'weight' in pn or 'bias' in pn:
|
|
split_param_col_tp1d(param, pg)
|
|
param.compute_spec.set_output_replicate(False)
|
|
else:
|
|
raise RuntimeError
|
|
elif 'mlp.c_proj' in mn:
|
|
if 'weight' in pn:
|
|
split_param_row_tp1d(param, pg)
|
|
else:
|
|
assert 'bias' in pn
|
|
elif 'wte' in mn or 'wpe' in mn:
|
|
assert 'weight' in pn
|
|
split_param_col_tp1d(param, pg)
|
|
elif 'c_attn' in mn or 'c_proj' in mn:
|
|
split_param_col_tp1d(param, pg)
|
|
# debug_print([0], '\t', param.compute_spec, param.shape)
|
|
|
|
|
|
def check_param_equal(model, torch_model, pg: ProcessGroup):
|
|
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
|
|
assert pg.tp_local_rank() is not None, f"{pg.rank()} {pg.tp_world_size()} {pg._tp_degree} {pg.tp_local_rank()}1"
|
|
assert pg.tp_world_size() is not None
|
|
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
|
|
|
|
|
|
def check_grad_equal(model, torch_model, pg: ProcessGroup):
|
|
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
|
|
assert tensor_shard_equal(torch_p.grad, p.grad, pg.tp_local_rank(), pg.tp_world_size())
|
|
|
|
|
|
def run_gpt(init_spec_func, use_ddp):
|
|
world_size = torch.distributed.get_world_size()
|
|
|
|
# build a PG with TP and DP hybrid
|
|
pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1))
|
|
|
|
# set seed make processes of the same tp group use the same seed
|
|
# set_seed(pg.tp_local_rank())
|
|
|
|
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
|
|
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
|
|
|
# make sure torch_model and model has the same parameter values
|
|
with ColoInitContext(device=get_current_device()):
|
|
model = model_builder()
|
|
model = model.cuda()
|
|
torch_model = model_builder().cuda()
|
|
|
|
if use_ddp:
|
|
torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
|
|
model = ColoDDP(model, process_group=pg)
|
|
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
torch_p.data.copy_(p)
|
|
|
|
init_spec_func(model, pg)
|
|
|
|
check_param_equal(model, torch_model, pg)
|
|
|
|
# close the dropout in eval mode
|
|
model.eval()
|
|
torch_model.eval()
|
|
set_seed(pg.dp_local_rank())
|
|
torch.distributed.barrier()
|
|
for i, (input_ids, label) in enumerate(train_dataloader):
|
|
colo_input = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
|
|
logits = model(colo_input)
|
|
torch_logits = torch_model(input_ids)
|
|
assert tensor_equal(torch_logits, logits), f"{torch_logits - logits}"
|
|
loss = criterion(logits, input_ids)
|
|
torch_loss = criterion(torch_logits, input_ids)
|
|
if use_ddp:
|
|
model.backward(loss)
|
|
else:
|
|
loss.backward()
|
|
torch_loss.backward()
|
|
check_grad_equal(model, torch_model, pg)
|
|
if i > 0:
|
|
break
|
|
set_seed(313)
|
|
|
|
|
|
def run_dist(rank, world_size, port, use_ddp):
|
|
if use_ddp and world_size == 1:
|
|
return
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
# Comments below tests for speed concern
|
|
# run_gpt(init_1d_row_spec, use_ddp)
|
|
# run_gpt(init_1d_col_spec, use_ddp)
|
|
run_gpt(init_megatron_spec, use_ddp)
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 4])
|
|
@pytest.mark.parametrize('use_ddp', [False, True])
|
|
@rerun_if_address_is_in_use()
|
|
def test_gpt(world_size, use_ddp):
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_gpt(4, use_ddp=False)
|