ColossalAI/tests/test_shardformer/test_model/test_shard_bert.py

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import pytest
import torch
from torch import distributed as dist
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import 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_from_hybrid_plugin,
check_all_grad_tensors,
check_loss,
check_output_hidden_state,
check_weight,
get_grad_tensors_for_check,
run_forward_backward_with_hybrid_plugin,
unwrap_model,
)
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)
org_loss, org_output, sharded_loss, sharded_output = \
run_forward_backward_with_hybrid_plugin(
org_model,
sharded_model,
sharded_optimizer,
data_gen_fn,
output_transform_fn,
criterion,
booster)
stage_manager = booster.plugin.stage_manager
tp_group = booster.plugin.tp_group
bert = unwrap_model(org_model, 'BertModel', 'bert')
sharded_bert = unwrap_model(sharded_model, 'BertModel', 'bert')
col_layer_for_check = ['encoder.layer[0].output.dense']
row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense']
# Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
grads_to_check = {}
if test_config['precision'] == 'fp32':
atol, rtol = 1e-4, 1e-3
else:
atol, rtol = 5e-3, 5e-3
if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
col_layer_grads = get_grad_tensors_for_check(bert,
sharded_bert,
col_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=1,
verbose=False)
row_layer_grads = get_grad_tensors_for_check(bert,
sharded_bert,
row_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=0,
verbose=False)
grads_to_check.update(col_layer_grads)
grads_to_check.update(row_layer_grads)
# optimizer executes step
org_optimizer.step()
sharded_optimizer.step()
# check last hidden state & loss
if stage_manager is None or stage_manager.is_last_stage():
if test_config['precision'] == 'fp32':
atol, rtol = 1e-5, 1e-3
else:
atol, rtol = 5e-3, 5e-3
if org_model.__class__.__name__ == 'BertModel':
check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
# check weights
if test_config['precision'] == 'fp32':
atol, rtol = 5e-3, 1e-3
else:
atol, rtol = 5e-3, 5e-3
if stage_manager is None or stage_manager.is_first_stage():
check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
# check grads
check_all_grad_tensors(grads_to_check)
torch.cuda.empty_cache()
@parameterize('test_config', [{
'tp_size': 1,
'pp_size': 2,
'num_microbatches': 4,
'use_lazy_init': True,
'precision': 'fp32',
}, {
'tp_size': 2,
'pp_size': 2,
'num_microbatches': 2,
'enable_all_optimization': True,
'use_lazy_init': True,
'precision': 'fp16',
'initial_scale': 1,
}, {
'tp_size': 4,
'pp_size': 1,
'enable_all_optimization': True,
'use_lazy_init': False,
'precision': 'fp32',
}, {
'tp_size': 2,
'pp_size': 1,
'enable_all_optimization': True,
'use_lazy_init': False,
'precision': 'fp32'
}, {
'tp_size': 2,
'pp_size': 1,
'enable_all_optimization': True,
'use_lazy_init': True,
'zero_stage': 2,
'precision': 'fp16',
'initial_scale': 1
}, {
'tp_size': 1,
'pp_size': 2,
'num_microbatches': 2,
'enable_all_optimization': True,
'use_lazy_init': True,
'zero_stage': 1,
'precision': 'fp16',
'initial_scale': 1
}])
def run_bert_test(test_config):
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():
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
clear_layout_converter()
Randomizer.reset_index()
torch.cuda.empty_cache()
@parameterize('test_config', [
{
'tp_size': 2,
'pp_size': 2,
'num_microbatches': 4,
'enable_all_optimization': False,
'use_lazy_init': False,
'precision': 'fp32',
},
{
'tp_size': 2,
'pp_size': 2,
'num_microbatches': 4,
'enable_all_optimization': False,
'use_lazy_init': False,
'precision': 'fp16',
'zero_stage': 1,
'initial_scale': 1,
},
])
def run_bert_3d_test(test_config):
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():
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
clear_layout_converter()
Randomizer.reset_index()
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()
def check_bert_3d(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_bert_3d_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_bert():
spawn(check_bert, 4)
@pytest.mark.largedist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_bert_3d():
spawn(check_bert_3d, 8)
if __name__ == "__main__":
test_bert()
test_bert_3d()