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[hotfix] test model unittest hotfix #1281

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Merged
feifeibear merged 10 commits into from
Jul 12, 2022
Merged

[hotfix] test model unittest hotfix #1281

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86be744
make it faster
feifeibear Jul 6, 2022
4b2333e
Merge branch 'main' of github.com:hpcaitech/ColossalAI into main
feifeibear Jul 7, 2022
5078a72
Merge branch 'main' of github.com:hpcaitech/ColossalAI into main
feifeibear Jul 7, 2022
73069e2
Merge branch 'main' of github.com:hpcaitech/ColossalAI into main
feifeibear Jul 11, 2022
371aa27
Merge branch 'main' of github.com:hpcaitech/ColossalAI into main
feifeibear Jul 12, 2022
9c504af
[hotfix] torchvison fx tests
feifeibear Jul 12, 2022
0488f3a
[hotfix] rename duplicated named test_gpt.py
feifeibear Jul 12, 2022
f919ae7
Merge branch 'main' of github.com:hpcaitech/ColossalAI into hotfix/fx
feifeibear Jul 12, 2022
2709969
Merge branch 'main' of github.com:hpcaitech/ColossalAI into hotfix/fx
feifeibear Jul 12, 2022
c23dfbc
[hotfix] test model unittest hotfix
feifeibear Jul 12, 2022
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17 changes: 9 additions & 8 deletions tests/test_tensor/test_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
from colossalai.utils import free_port
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.tensor import ShardSpec, ColoTensorSpec, ComputePattern, \
ComputeSpec, ColoTensor, DistSpecManager, ProcessGroup
ComputeSpec, ColoTensor, DistSpecManager, ProcessGroup, ReplicaSpec
from colossalai.nn.optimizer import ColoOptimizer

from tests.components_to_test.registry import non_distributed_component_funcs
Expand Down Expand Up @@ -76,22 +76,23 @@ def run_1d_hybrid_tp(model_name):
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
# print(name)

# num_class = type_vocab_size = 2 | (8, 2)
# TODO(jiaruifang) has bug if open the following 2 comments
if 'classifier' in name and 'weight' in name:
init_1d_row_linear(p, pg)
# num_class = vocab_size = 30524 | (30524, 8)
if 'word_embeddings' in name and 'weight' in name:
elif 'word_embeddings' in name and 'weight' in name:
init_1d_row_embedding(p, pg)
# num_class = seq_len = 512 | (512, 8)
if 'position_embeddings' in name and 'weight' in name:
elif 'position_embeddings' in name and 'weight' in name:
init_1d_row_embedding(p, pg)
# num_class = type_vocab_size = 2 | (2, 8)
if 'token_type_embeddings' in name and 'weight' in name:
elif 'token_type_embeddings' in name and 'weight' in name:
init_1d_col_embedding(p, pg)
if p.process_group.tp_world_size() == 1:
p.set_process_group(pg)
elif p.process_group.tp_world_size() == 1:
with DistSpecManager.no_grad():
p.redistribute(ReplicaSpec(), pg)

elif "simple_net" == model_name:
# A naive way to set spec for all weights in Linear
for name, p in model.named_parameters():
Expand Down