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[test] Hotfix/fix some model test and refactor check util api (#4369)

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* fix llama test

* fix test bug of bert, blip2, bloom, gpt2

* fix llama test

* fix opt test

* fix sam test

* fix sam test

* fix t5 test

* fix vit test

* fix whisper test

* fix whisper test

* polish code

* adjust allclose parameter

* Add mistakenly deleted code

* addjust allclose

* change loss function for some base model
This commit is contained in:
Bin Jia
2023-08-03 14:51:36 +08:00
committed by Hongxin Liu
parent c3ca53cf05
commit 5c6f183192
16 changed files with 135 additions and 336 deletions
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39
tests/test_shardformer/test_model/test_shard_bloom.py
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@@ -3,7 +3,6 @@ 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,
@@ -12,7 +11,7 @@ from colossalai.testing import (
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward
from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
@@ -26,7 +25,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
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}"
atol=1e-6), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# unwrap model
if org_model.__class__.__name__ == 'BloomModel':
@@ -36,35 +35,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
bloom = org_model.transformer
sharded_bloom = sharded_model.transformer
# check attention grad
org_grad = bloom.h[0].self_attention.query_key_value.weight.grad
shard_grad = sharded_bloom.h[0].self_attention.query_key_value.weight.grad
shard_weight = sharded_bloom.h[0].self_attention.query_key_value.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)]
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}"
# check embedding weights
org_grad = bloom.word_embeddings.weight.grad
shard_grad = sharded_bloom.word_embeddings.weight.grad
shard_weight = sharded_bloom.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)]
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}"
# check grad
col_layer_for_check = ['h[0].self_attention.query_key_value']
row_layer_for_check = ['h[0].self_attention.dense']
check_grad(bloom, sharded_bloom, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
check_grad(bloom, sharded_bloom, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])