github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-17 23:18:36 +00:00
Code Issues Packages Projects Releases Wiki Activity

[shardformer] update tests for all optimization (#4413)

Browse Source 
[shardformer] update tests for all optimization
This commit is contained in:
flybird11111
2023-08-11 16:40:06 +08:00
committed by Hongxin Liu
parent 7711bd524a
commit 1edc9b5fb3
3 changed files with 50 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
tests/kit/model_zoo/transformers/bert.py
View File

@@ -69,21 +69,30 @@ def data_gen_for_mcq():
# data['labels'] = torch.tensor([0], dtype=torch.int64)
input_ids = torch.tensor([[[
101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591,
4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102
4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102, 5442,
1012, 102, 102
],
[
101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037,
4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096,
2218, 1999, 1996, 2192, 1012, 102, 0, 0
2218, 1999, 1996, 2192, 1012, 102, 0, 0, 1012, 102, 0, 0
]]])
token_type_ids = torch.tensor(
[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0]]])
attention_mask = torch.tensor(
[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0]]])
token_type_ids = torch.tensor([[[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
]]])
attention_mask = torch.tensor([[[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1
],
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
]]])
labels = torch.tensor([0], dtype=torch.int64)
return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)

39
tests/test_shardformer/test_model/test_shard_bert.py
View File

@@ -36,10 +36,14 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
tp_group = booster.plugin.tp_group
# 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=1e-5, rtol=1e-3)
check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3)
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
# unwrap model
if org_model.__class__.__name__ == 'BertModel':
bert = org_model
@@ -51,17 +55,25 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
col_layer_for_check = ['encoder.layer[0].output.dense']
row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense']
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():
#check_weight(bert.embeddings.word_embeddings, sharded_bert.embeddings.word_embeddings, tp_group, atol=1e-5, rtol=1e-3)
#check_weight(bert.encoder.layer[0].attention.self.query, sharded_bert.encoder.layer[0].attention.self.query, tp_group, atol=5e-3, rtol=1e-3)
check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False)
check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False)
check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False)
# check weights after optimizer.step()
org_optimizer.step()
sharded_optimizer.step()
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=5e-3, rtol=1e-3, dim=1, verbose=False)
check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
torch.cuda.empty_cache()
@@ -70,23 +82,26 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
'tp_size': 1,
'pp_size': 2,
'num_microbatches': 4,
'use_lazy_init': True
'use_lazy_init': True,
'precision': 'fp32',
}, {
'tp_size': 2,
'pp_size': 2,
'num_microbatches': 4,
'enable_fused_normalization': False,
'use_lazy_init': False
'num_microbatches': 2,
'enable_all_optimization': True,
'use_lazy_init': True,
'precision': 'fp16',
'initial_scale': 1,
}, {
'tp_size': 4,
'pp_size': 1,
'enable_fused_normalization': True,
'use_lazy_init': False
'enable_all_optimization': True,
'use_lazy_init': False,
'precision': 'fp32',
}])
def run_bert_test(test_config):
sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
test_config['precision'] = 'float'
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)