mirror of https://github.com/hpcaitech/ColossalAI
201 lines
7.3 KiB
Python
201 lines
7.3 KiB
Python
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.testing import assert_close
|
|
|
|
import colossalai
|
|
from colossalai.legacy.amp import convert_to_apex_amp
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
|
from colossalai.utils import set_seed
|
|
from colossalai.utils.cuda import get_current_device
|
|
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
|
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
|
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
|
|
|
PLACEMENT_CONFIGS = [
|
|
{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 0.0}, # zero2
|
|
{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 1.0}, # zero2-offload
|
|
{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 0.5}, # zero2-offload-half
|
|
{"placement_policy": "static", "shard_param_frac": 1.0}, # zero3
|
|
{"placement_policy": "static", "shard_param_frac": 0.5}, # zero3-half
|
|
{
|
|
"placement_policy": "static",
|
|
"shard_param_frac": 1.0,
|
|
"offload_optim_frac": 1.0,
|
|
"offload_param_frac": 1.0,
|
|
}, # zero3-offload-all
|
|
{"placement_policy": "auto"},
|
|
]
|
|
|
|
# this model is large enough to slice to chunks
|
|
TEST_MODELS = ["transformers_gpt_lm"]
|
|
# these models are too small, all parameters in these models are compacted into one chunk
|
|
EXAMPLE_MODELS = [
|
|
"transformers_bert_for_sequence_classification",
|
|
"custom_hanging_param_model",
|
|
"custom_nested_model",
|
|
"custom_repeated_computed_layers",
|
|
]
|
|
|
|
# bfloat16 cannot represent them exactly
|
|
BF16_IGNORED_KEYS = [
|
|
"masked_bias",
|
|
]
|
|
|
|
|
|
def check_param(model: GeminiDDP, torch_model: torch.nn.Module, dtype: torch.dtype):
|
|
zero_dict = model.state_dict(only_rank_0=False)
|
|
torch_dict = torch_model.state_dict()
|
|
|
|
for key, value in torch_dict.items():
|
|
# key is 'module.model.PARAMETER', so we truncate it
|
|
key = key[7:]
|
|
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
|
|
temp_zero_value = zero_dict[key].to(device=value.device)
|
|
if dtype is torch.bfloat16 and any(k in key for k in BF16_IGNORED_KEYS):
|
|
continue
|
|
rtol, atol = 2e-3, 6e-3
|
|
if dtype is torch.bfloat16:
|
|
rtol, atol = 4e-3, 8e-3
|
|
# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
|
|
assert_close(
|
|
value.float(),
|
|
temp_zero_value.float(),
|
|
rtol=rtol,
|
|
atol=atol,
|
|
msg=lambda s: s + f"\n{key}\n{temp_zero_value.dtype}",
|
|
)
|
|
|
|
|
|
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
|
@parameterize("model_name", TEST_MODELS)
|
|
@parameterize("mixed_precision", [torch.half, torch.bfloat16])
|
|
@parameterize("master_weights", [True, False])
|
|
def exam_model_step(placement_config, model_name: str, mixed_precision: torch.dtype, master_weights: bool):
|
|
set_seed(42)
|
|
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
|
iter(model_zoo.get_sub_registry(model_name).values())
|
|
)
|
|
|
|
torch_model = model_builder().cuda()
|
|
# apex no master weights leads to nan, so we don't use it
|
|
amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=128)
|
|
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
|
|
torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
|
|
torch_model = DDP(torch_model, device_ids=[dist.get_rank()])
|
|
|
|
model = model_builder().cuda()
|
|
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
p.data.copy_(torch_p.data)
|
|
|
|
world_size = torch.distributed.get_world_size()
|
|
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
|
|
config_dict[world_size]["chunk_size"] = 5000
|
|
config_dict[world_size]["keep_gathered"] = False
|
|
model = GeminiDDP(
|
|
model, config_dict, **placement_config, mixed_precision=mixed_precision, master_weights=master_weights
|
|
)
|
|
|
|
optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
|
zero_optim = GeminiOptimizer(optimizer, model, initial_scale=128)
|
|
|
|
model.eval()
|
|
torch_model.eval()
|
|
|
|
set_seed(dist.get_rank() * 3 + 128)
|
|
rtol, atol = 4e-2, 4e-2
|
|
train_dataloader = iter(DummyDataloader(data_gen_fn))
|
|
for i, data in enumerate(train_dataloader):
|
|
if i > 2:
|
|
break
|
|
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
|
zero_optim.zero_grad()
|
|
torch_optim.zero_grad()
|
|
|
|
torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
|
loss = run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
|
# as no master weights leads to error accumulation, we don't check the loss
|
|
if master_weights:
|
|
assert_close(torch_loss.float(), loss.float(), rtol=rtol, atol=atol)
|
|
|
|
zero_optim.step()
|
|
torch_optim.step()
|
|
|
|
if master_weights:
|
|
check_param(model, torch_model, mixed_precision)
|
|
|
|
|
|
@parameterize("placement_config", [PLACEMENT_CONFIGS[3]])
|
|
@parameterize("model_name", EXAMPLE_MODELS)
|
|
@parameterize("mixed_precision", [torch.half])
|
|
def exam_tiny_example(placement_config, model_name: str, mixed_precision: torch.dtype):
|
|
set_seed(2008)
|
|
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
|
iter(model_zoo.get_sub_registry(model_name).values())
|
|
)
|
|
|
|
torch_model = model_builder().cuda()
|
|
amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=2)
|
|
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
|
|
torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
|
|
torch_model = DDP(torch_model, device_ids=[dist.get_rank()])
|
|
|
|
model = model_builder().cuda()
|
|
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
p.data.copy_(torch_p.data)
|
|
|
|
model = GeminiDDP(
|
|
model,
|
|
chunk_init_device=get_current_device(),
|
|
search_range_m=1,
|
|
pin_memory=True,
|
|
mixed_precision=mixed_precision,
|
|
**placement_config,
|
|
)
|
|
optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
|
zero_optim = GeminiOptimizer(optimizer, model, initial_scale=2)
|
|
|
|
model.eval()
|
|
torch_model.eval()
|
|
|
|
set_seed(dist.get_rank() * 3 + 128)
|
|
|
|
train_dataloader = DummyDataloader(data_gen_fn)
|
|
for i, data in enumerate(train_dataloader):
|
|
if i > 2:
|
|
break
|
|
|
|
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
|
|
|
zero_optim.zero_grad()
|
|
torch_optim.zero_grad()
|
|
|
|
run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
|
run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
|
zero_optim.step()
|
|
torch_optim.step()
|
|
|
|
check_param(model, torch_model, mixed_precision)
|
|
|
|
|
|
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_model_step()
|
|
exam_tiny_example()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [1, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_optim(world_size):
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_optim(1)
|