mirror of https://github.com/hpcaitech/ColossalAI
40 lines
1.8 KiB
Python
40 lines
1.8 KiB
Python
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]
|
|
}
|
|
|
|
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
|
|
|
|
|
|
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]]]
|
|
}
|
|
|
|
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])
|
|
|
|
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
|