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ColossalAI/tests/test_shardformer/test_model/test_shard_whisper.py

81 lines
3.3 KiB

import pytest
import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.testing import (
assert_hf_output_close,
clear_cache_before_run,
parameterize,
rerun_if_address_is_in_use,
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
# check forward
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values', atol=1e-5)
# do backward
org_loss.backward()
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}"
# unwarp the model
if org_model.__class__.__name__ == 'WhisperForConditionalGeneration':
whisper = org_model.model
sharded_whisper = sharded_model.model
else:
whisper = org_model
sharded_whisper = sharded_model
# check grad
if org_model.__class__.__name__ == 'WhisperForAudioClassification':
col_layer_for_check = ['encoder.layers[0].self_attn.q_proj']
row_layer_for_check = ['encoder.layers[0].self_attn.out_proj']
else:
col_layer_for_check = ['encoder.layers[0].self_attn.q_proj', 'decoder.layers[0].self_attn.q_proj']
row_layer_for_check = ['encoder.layers[0].self_attn.out_proj', 'decoder.layers[0].self_attn.out_proj']
check_grad(whisper, sharded_whisper, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False)
check_grad(whisper, sharded_whisper, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False)
@parameterize('enable_fused_normalization', [True, False])
@parameterize('enable_tensor_parallelism', [True, False])
@parameterize('enable_flash_attention', [True, False])
@parameterize('enable_jit_fused', [True, False])
def run_whisper_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused):
sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_model(model_fn,
enable_fused_normalization=enable_fused_normalization,
enable_tensor_parallelism=enable_tensor_parallelism,
enable_flash_attention=enable_flash_attention,
enable_jit_fused=enable_jit_fused)
check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
torch.cuda.empty_cache()
def check_whisper(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_whisper_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_whisper():
spawn(check_whisper, 2)
if __name__ == "__main__":
test_whisper()