ColossalAI/tests/test_shardformer/test_model/test_shard_bert.py

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import pytest
import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
parameterize,
rerun_if_address_is_in_use,
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
# check forward
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
assert_hf_output_close(org_output, shard_output)
# do backward
org_loss.backward()
shard_loss.backward()
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# check grad
if org_model.__class__.__name__ == 'BertModel':
bert = org_model
sharded_bert = sharded_model
else:
bert = org_model.bert
sharded_bert = sharded_model.bert
# compare self attention grad
org_grad = bert.encoder.layer[0].attention.self.query.weight.grad
shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad
shard_weight = sharded_bert.encoder.layer[0].attention.self.query.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
# compare embedding grad
org_grad = bert.embeddings.word_embeddings.weight.grad
shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad
shard_weight = sharded_bert.embeddings.word_embeddings.weight
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
else:
all_shard_grad = shard_grad
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
@parameterize('enable_fused_normalization', [False, True])
@parameterize('enable_tensor_parallelism', [False, True])
@parameterize('use_lazy_init', [False, True])
def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism,
use_lazy_init)
check_state_dict(org_model, sharded_model, name=name)
check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
torch.cuda.empty_cache()
def check_bert(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_bert_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_bert():
spawn(check_bert, 2)
if __name__ == "__main__":
test_bert()