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[zero] fix unit-tests (#2039)

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HELSON
2022-11-30 10:40:31 +08:00
committed by GitHub
parent eb7742a4bb
commit 17a3c685b0
4 changed files with 44 additions and 44 deletions
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69
tests/test_gemini/update/test_optim.py
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@@ -6,6 +6,7 @@ import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.testing import assert_close
import colossalai
from colossalai.amp import convert_to_apex_amp
@@ -20,7 +21,7 @@ from colossalai.utils.cuda import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from tests.components_to_test import run_fwd_bwd
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import set_seed
from tests.test_tensor.common_utils import debug_print, set_seed
def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
@@ -35,27 +36,31 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
assert torch.allclose(value, temp_zero_value, rtol=1e-3, atol=1e-2), "parameter '{}' has problem.".format(key)
assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-2)
# 'gpt2', 'bert',
TEST_MODELS = ['gpt2', 'bert']
# TEST_MODELS = ['simple_net']
EXAMPLE_MODELS = ['simple_net']
@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
@parameterize('placement_policy', ['cuda'])
@parameterize('model_name', TEST_MODELS)
def exam_model_step(placement_policy, model_name: str):
set_seed(42)
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
torch_model = model_builder().cuda()
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()])
with ColoInitContext(device=get_current_device()):
model = model_builder()
torch_model = model_builder().cuda()
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p.data)
p.data.copy_(torch_p.data)
world_size = torch.distributed.get_world_size()
config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
@@ -70,12 +75,7 @@ def exam_model_step(placement_policy, model_name: str):
model = ZeroDDP(model, gemini_manager, pin_memory=True)
optimizer = HybridAdam(model.parameters(), lr=1e-3)
zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2)
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
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()])
zero_optim = ZeroOptimizer(optimizer, model, initial_scale=128)
model.eval()
torch_model.eval()
@@ -84,15 +84,13 @@ def exam_model_step(placement_policy, model_name: str):
for i, (input_ids, label) in enumerate(train_dataloader):
if i > 2:
break
input_ids, label = input_ids.cuda(), label.cuda()
zero_optim.zero_grad()
torch_optim.zero_grad()
torch_loss = run_fwd_bwd(torch_model, input_ids.cuda(), label.cuda(), criterion, use_init_ctx=False)
loss = run_fwd_bwd(model, input_ids.cuda(), label.cuda(), criterion, use_init_ctx=True)
assert torch.allclose(torch_loss, loss, rtol=1e-3, atol=1e-2), f"{torch_loss} vs {loss}"
# debug_print([0], zero_logits, torch_logits)
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
assert_close(torch_loss, loss)
zero_optim.step()
torch_optim.step()
@@ -101,31 +99,29 @@ def exam_model_step(placement_policy, model_name: str):
@parameterize('placement_policy', ['cuda', 'cpu'])
@parameterize('model_name', TEST_MODELS)
@parameterize('model_name', EXAMPLE_MODELS)
def exam_tiny_example(placement_policy, model_name: str):
set_seed(42)
set_seed(2008)
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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()])
with ColoInitContext(device=get_current_device()):
model = model_builder()
torch_model = model_builder().cuda()
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p.data)
p.data.copy_(torch_p.data)
chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_mb=1)
gemini_manager = GeminiManager(placement_policy, chunk_manager)
model = ZeroDDP(model, gemini_manager, pin_memory=True)
optimizer = HybridAdam(model.parameters(), lr=1e-3)
zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2)
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
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.eval()
torch_model.eval()
@@ -134,14 +130,15 @@ def exam_tiny_example(placement_policy, model_name: str):
if i > 2:
break
input_ids = input_ids.cuda()
label = label.cuda()
zero_optim.zero_grad()
torch_optim.zero_grad()
torch_loss = run_fwd_bwd(torch_model, input_ids.cuda(), label.cuda(), criterion, use_init_ctx=False)
loss = run_fwd_bwd(model, input_ids.cuda(), label.cuda(), criterion, use_init_ctx=True)
assert torch.allclose(torch_loss, loss, rtol=1e-3, atol=1e-2), f"{torch_loss} vs {loss}"
# debug_print([0], zero_logits, torch_logits)
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
assert_close(torch_loss, loss)
zero_optim.step()
torch_optim.step()
@@ -165,4 +162,4 @@ def test_optim(world_size):
if __name__ == '__main__':
test_optim(2)
test_optim(1)