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add test for test_validation_step #4

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Aug 4, 2023
Merged

add test for test_validation_step #4

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36 changes: 28 additions & 8 deletions tests/collections/multimodal/test_speechllm_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,15 @@
)


def setup_module():
# init model parallel needed for LLM loss
init_method = 'tcp://'
master_ip = 'localhost'
master_port = '6000'
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(backend='gloo', world_size=1, rank=0, init_method=init_method)
parallel_state.initialize_model_parallel(1, 1)

@pytest.fixture
def llm_model_config():
this_test_dir = os.path.dirname(os.path.abspath(__file__))
Expand Down Expand Up @@ -113,7 +122,6 @@ def perception_model_config():
)
return model_config


class TestModularizedSpeechGPTModel:
@pytest.mark.unit
def test_init_and_train(
Expand All @@ -137,13 +145,6 @@ def test_training_step(
trainer, llm_model_config.trainer = trainer_config
model = ModularizedSpeechGPTModel(cfg=llm_model_config.model, trainer=trainer)
model.cuda()
# init model parallel needed for LLM loss
init_method = 'tcp://'
master_ip = 'localhost'
master_port = '6000'
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(backend='gloo', world_size=1, rank=0, init_method=init_method)
parallel_state.initialize_model_parallel(1, 1)

model.train()
pl.seed_everything(1)
Expand All @@ -155,4 +156,23 @@ def test_training_step(
loss_mean = model.training_step(batch, None)
assert np.allclose(loss_mean.cpu().detach().numpy(), 5.1678314)

@pytest.mark.unit
def test_validation_step(
self, llm_model_config, perception_model_config, trainer_config
):
llm_model_config.model.perception = perception_model_config
trainer, llm_model_config.trainer = trainer_config
model = ModularizedSpeechGPTModel(cfg=llm_model_config.model, trainer=trainer)
model.cuda()

model.train()
pl.seed_everything(1)
signal = torch.randn(2, 64000)
signal_len = torch.from_numpy(np.array([64000, 64000]))
transcript = torch.randn(2, 4).int()
transcript_length = torch.from_numpy(np.array([3, 2]))
batch = signal, signal_len, transcript, transcript_length
loss_mean = model.validation_step(batch, None)
assert np.allclose(loss_mean['loss'].cpu().detach().numpy(), 5.1694)

# TODO(zhehuai): test ckpt restore