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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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42
tests/test_pipeline/test_pipeline_utils/test_t5_pipeline_utils.py
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@@ -4,36 +4,42 @@ from colossalai.shardformer.policies.t5 import T5BasePolicy
def test_t5_pipeline_distribution():
num_test_cases = 8
test_dict = {
'num_encoder_layers': [2, 1, 3, 2, 3, 2, 10, 5],
'num_decoder_layers': [2, 8, 0, 2, 1, 5, 6, 22],
'num_stages': [2, 2, 2, 4, 4, 4, 8, 8],
'decoder_starting_stage': [1, 1, 2, 2, 3, 1, 5, 2]
"num_encoder_layers": [2, 1, 3, 2, 3, 2, 10, 5],
"num_decoder_layers": [2, 8, 0, 2, 1, 5, 6, 22],
"num_stages": [2, 2, 2, 4, 4, 4, 8, 8],
"decoder_starting_stage": [1, 1, 2, 2, 3, 1, 5, 2],
}
for i in range(num_test_cases):
_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(test_dict['num_encoder_layers'][i],
test_dict['num_decoder_layers'][i],
test_dict['num_stages'][i])
assert test_dict['decoder_starting_stage'][i] == decoder_starting_stage
_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
)
assert test_dict["decoder_starting_stage"][i] == decoder_starting_stage
def test_t5_pipeline_layers():
num_test_cases = 4
test_dict = {
'num_encoder_layers': [2, 3, 2, 4],
'num_decoder_layers': [2, 0, 2, 8],
'num_stages': [2, 2, 4, 4],
'layers_per_stage': [[[0, 2], [0, 2]], [[0, 1], [1, 3]], [[0, 1], [1, 2], [0, 1], [1, 2]],
[[0, 4], [0, 3], [3, 6], [6, 8]]]
"num_encoder_layers": [2, 3, 2, 4],
"num_decoder_layers": [2, 0, 2, 8],
"num_stages": [2, 2, 4, 4],
"layers_per_stage": [
[[0, 2], [0, 2]],
[[0, 1], [1, 3]],
[[0, 1], [1, 2], [0, 1], [1, 2]],
[[0, 4], [0, 3], [3, 6], [6, 8]],
],
}
for i in range(num_test_cases):
layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
test_dict['num_encoder_layers'][i], test_dict['num_decoder_layers'][i], test_dict['num_stages'][i])
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
)
for stage in range(test_dict['num_stages'][i]):
start_idx, end_idx = test_dict['layers_per_stage'][i][stage]
predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage,
decoder_starting_stage)
for stage in range(test_dict["num_stages"][i]):
start_idx, end_idx = test_dict["layers_per_stage"][i][stage]
predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(
layers_per_stage, stage, decoder_starting_stage
)
assert start_idx == predicted_start
assert end_idx == predicted_end

44
tests/test_pipeline/test_pipeline_utils/test_whisper_pipeline_utils.py
View File

@@ -4,41 +4,47 @@ from colossalai.shardformer.policies.whisper import WhisperPolicy
def test_whisper_pipeline_distribution():
num_test_cases = 8
test_dict = {
'num_encoder_layers': [2, 1, 3, 2, 3, 2, 10, 5],
'num_decoder_layers': [2, 8, 0, 2, 1, 5, 6, 22],
'num_stages': [2, 2, 2, 4, 4, 4, 8, 8],
'decoder_starting_stage': [1, 1, 2, 2, 3, 1, 5, 2]
"num_encoder_layers": [2, 1, 3, 2, 3, 2, 10, 5],
"num_decoder_layers": [2, 8, 0, 2, 1, 5, 6, 22],
"num_stages": [2, 2, 2, 4, 4, 4, 8, 8],
"decoder_starting_stage": [1, 1, 2, 2, 3, 1, 5, 2],
}
for i in range(num_test_cases):
_, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(test_dict['num_encoder_layers'][i],
test_dict['num_decoder_layers'][i],
test_dict['num_stages'][i])
assert test_dict['decoder_starting_stage'][i] == decoder_starting_stage
_, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
)
assert test_dict["decoder_starting_stage"][i] == decoder_starting_stage
def test_whisper_pipeline_layers():
num_test_cases = 4
test_dict = {
'num_encoder_layers': [2, 3, 2, 4],
'num_decoder_layers': [2, 0, 2, 8],
'num_stages': [2, 2, 4, 4],
'layers_per_stage': [[[0, 2], [0, 2]], [[0, 1], [1, 3]], [[0, 1], [1, 2], [0, 1], [1, 2]],
[[0, 4], [0, 3], [3, 6], [6, 8]]]
"num_encoder_layers": [2, 3, 2, 4],
"num_decoder_layers": [2, 0, 2, 8],
"num_stages": [2, 2, 4, 4],
"layers_per_stage": [
[[0, 2], [0, 2]],
[[0, 1], [1, 3]],
[[0, 1], [1, 2], [0, 1], [1, 2]],
[[0, 4], [0, 3], [3, 6], [6, 8]],
],
}
for i in range(num_test_cases):
layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
test_dict['num_encoder_layers'][i], test_dict['num_decoder_layers'][i], test_dict['num_stages'][i])
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
)
for stage in range(test_dict['num_stages'][i]):
start_idx, end_idx = test_dict['layers_per_stage'][i][stage]
predicted_start, predicted_end = WhisperPolicy.get_whisper_stage_index(layers_per_stage, stage,
decoder_starting_stage)
for stage in range(test_dict["num_stages"][i]):
start_idx, end_idx = test_dict["layers_per_stage"][i][stage]
predicted_start, predicted_end = WhisperPolicy.get_whisper_stage_index(
layers_per_stage, stage, decoder_starting_stage
)
assert start_idx == predicted_start
assert end_idx == predicted_end
if __name__ == '__main__':
if __name__ == "__main__":
test_whisper_pipeline_distribution()
test_whisper_pipeline_layers()