github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-08 12:30:42 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

37
tests/test_shardformer/test_model/test_shard_llama.py
View File

@@ -5,7 +5,6 @@ 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,
@@ -14,7 +13,7 @@ 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
os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
@@ -24,7 +23,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
output_transform_fn, loss_fn)
# forward check
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4)
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5)
# run backward
org_loss.backward()
@@ -41,33 +40,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
llama_model = org_model
shard_llama_model = sharded_model
# check attention grad
org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad
shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad
shard_weight = shard_llama_model.layers[0].self_attn.q_proj.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(4)]
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{shard_grad}"
# check embedding grad
org_grad = llama_model.embed_tokens.weight.grad
shard_grad = shard_llama_model.embed_tokens.weight.grad
shard_weight = shard_llama_model.embed_tokens.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(4)]
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{shard_grad}"
# check grad
col_layer_for_check = ['layers[0].self_attn.q_proj', 'embed_tokens']
row_layer_for_check = ['layers[0].self_attn.o_proj']
check_grad(llama_model, shard_llama_model, col_layer_for_check, atol=1e-6, rtol=1e-4, dim=0, verbose=False)
check_grad(llama_model, shard_llama_model, row_layer_for_check, atol=1e-6, rtol=1e-4, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])