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[shardformer] update bloom/llama/vit/chatglm tests (#4420)

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[shardformer] update bloom/llama/vit/chatglm tests

[shardformer] update opt tests

[shardformer] update opt tests

[shardformer] update bloom/llama/vit/chatglm tests

[shardformer] update bloom/llama/vit/chatglm tests

[shardformer] update bloom/llama/vit/chatglm tests
This commit is contained in:
flybird11111
2023-08-14 15:51:13 +08:00
committed by Hongxin Liu
parent 108e54a0b4
commit 328a791d10
6 changed files with 157 additions and 98 deletions
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43
tests/test_shardformer/test_model/test_shard_bloom.py
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@@ -36,11 +36,14 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
# 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__ == 'BloomModel':
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-6, rtol=1e-3)
check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
# unwrap model
if org_model.__class__.__name__ == 'BloomModel':
@@ -54,14 +57,22 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
row_layer_for_check = ['h[0].self_attention.query_key_value', 'word_embeddings']
col_layer_for_check = ['h[0].self_attention.dense']
if stage_manager is None or stage_manager.is_first_stage():
check_grad(bloom, sharded_bloom, row_layer_for_check, tp_group, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
check_grad(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
if test_config['precision'] == 'fp32':
atol, rtol = 1e-6, 1e-5
else:
atol, rtol = 5e-3, 5e-3
check_grad(bloom, sharded_bloom, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False)
check_grad(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
# check weights after optimizer.step()
org_optimizer.step()
sharded_optimizer.step()
if stage_manager is None or stage_manager.is_first_stage():
check_weight(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False)
if test_config['precision'] == 'fp32':
atol, rtol = 1e-4, 1e-3
else:
atol, rtol = 5e-3, 5e-3
check_weight(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
torch.cuda.empty_cache()
@@ -70,29 +81,29 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
'tp_size': 2,
'pp_size': 2,
'num_microbatches': 4,
'enable_fused_normalization': True,
'use_lazy_init': True
'enable_all_optimization': True,
'use_lazy_init': True,
'precision': 'fp16',
'initial_scale': 1,
}, {
'tp_size': 1,
'pp_size': 2,
'num_microbatches': 4,
'enable_fused_normalization': False,
'use_lazy_init': False
'enable_all_optimization': False,
'use_lazy_init': False,
'precision': 'fp32',
}, {
'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_bloom_test(test_config):
# TODO: add test_config for TP+DP after supporting & debugging it
# {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True}
# TODO: add test_config for flash attention & jit operator after supporting
sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom')
test_config['precision'] = 'float' # Do not use fp16/bf16 in testing
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)