github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-09 04:50:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

[test] merge old components to test to model zoo (#4945)

Browse Source 
* [test] add custom models in model zoo

* [test] update legacy test

* [test] update model zoo

* [test] update gemini test

* [test] remove components to test
This commit is contained in:
Hongxin Liu
2023-10-20 10:35:08 +08:00
committed by GitHub
parent 3a41e8304e
commit b8e770c832
49 changed files with 461 additions and 914 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
tests/test_zero/test_gemini/test_fwd_bwd.py
View File

@@ -12,8 +12,7 @@ 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.components_to_test import run_fwd_bwd
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
PLACEMENT_CONFIGS = [
{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
@@ -38,7 +37,7 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
@parameterize("placement_config", PLACEMENT_CONFIGS)
@parameterize("keep_gather", [False, True])
@parameterize("model_name", ["gpt2", "bert"])
@parameterize("model_name", ["transformers_gpt_lm"])
@parameterize("use_grad_checkpoint", [False, True])
@parameterize("master_weights", [False, True])
def exam_gpt_fwd_bwd(
@@ -49,17 +48,22 @@ def exam_gpt_fwd_bwd(
master_weights: bool = True,
):
init_device = get_current_device()
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
iter(model_zoo.get_sub_registry(model_name).values())
)
set_seed(42)
model = model_builder(use_grad_checkpoint)
model = model_builder()
set_seed(42)
torch_model = model_builder(use_grad_checkpoint).cuda()
torch_model = model_builder().cuda()
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p.data)
if use_grad_checkpoint:
model.gradient_checkpointing_enable()
torch_model.gradient_checkpointing_enable()
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
@@ -77,25 +81,22 @@ def exam_gpt_fwd_bwd(
torch_model = DDP(torch_model, device_ids=[rank])
set_seed(rank)
for i, (input_ids, label) in enumerate(train_dataloader):
# you can only test a single fwd + bwd.
# after bwd param is grad for Gemini, due to the chunk reuse optimization.
if i > 0:
break
input_ids, label = input_ids.cuda(), label.cuda()
torch_optim.zero_grad()
zero_optim.zero_grad()
data = data_gen_fn()
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
# set random seed is same as torch_model.eval()
set_seed(42)
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
set_seed(42)
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
torch_optim.zero_grad()
zero_optim.zero_grad()
assert torch.equal(torch_loss, loss)
# set random seed is same as torch_model.eval()
set_seed(42)
torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
set_seed(42)
loss = run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
check_grad(model, torch_model)
assert_close(torch_loss.float(), loss.float())
check_grad(model, torch_model)
def run_dist(rank, world_size, port):