117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
import time
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import numpy as np
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import pytest
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import torch
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import tests.base.develop_utils as tutils
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from pytorch_lightning import Trainer, seed_everything
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from tests.base.models import ParityModuleMNIST, ParityModuleRNN
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# TODO: explore where the time leak comes from
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@pytest.mark.parametrize('cls_model,max_diff', [
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(ParityModuleRNN, 0.05),
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(ParityModuleMNIST, 0.22)
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])
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_pytorch_parity(tmpdir, cls_model, max_diff):
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"""
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Verify that the same pytorch and lightning models achieve the same results
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"""
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num_epochs = 4
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num_rums = 3
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lightning_outs, pl_times = lightning_loop(cls_model, num_rums, num_epochs)
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manual_outs, pt_times = vanilla_loop(cls_model, num_rums, num_epochs)
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# make sure the losses match exactly to 5 decimal places
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for pl_out, pt_out in zip(lightning_outs, manual_outs):
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np.testing.assert_almost_equal(pl_out, pt_out, 5)
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# the fist run initialize dataset (download & filter)
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tutils.assert_speed_parity_absolute(pl_times[1:], pt_times[1:],
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nb_epochs=num_epochs, max_diff=max_diff)
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def vanilla_loop(cls_model, num_runs=10, num_epochs=10):
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"""
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Returns an array with the last loss from each epoch for each run
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"""
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device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
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errors = []
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times = []
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torch.backends.cudnn.deterministic = True
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for i in range(num_runs):
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time_start = time.perf_counter()
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# set seed
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seed = i
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seed_everything(seed)
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# init model parts
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model = cls_model()
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dl = model.train_dataloader()
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optimizer = model.configure_optimizers()
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# model to GPU
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model = model.to(device)
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epoch_losses = []
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# as the first run is skipped, no need to run it long
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for epoch in range(num_epochs if i > 0 else 1):
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# run through full training set
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for j, batch in enumerate(dl):
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batch = [x.to(device) for x in batch]
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loss_dict = model.training_step(batch, j)
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loss = loss_dict['loss']
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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# track last epoch loss
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epoch_losses.append(loss.item())
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time_end = time.perf_counter()
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times.append(time_end - time_start)
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errors.append(epoch_losses[-1])
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return errors, times
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def lightning_loop(cls_model, num_runs=10, num_epochs=10):
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errors = []
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times = []
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for i in range(num_runs):
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time_start = time.perf_counter()
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# set seed
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seed = i
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seed_everything(seed)
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model = cls_model()
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# init model parts
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trainer = Trainer(
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# as the first run is skipped, no need to run it long
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max_epochs=num_epochs if i > 0 else 1,
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progress_bar_refresh_rate=0,
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weights_summary=None,
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gpus=1,
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checkpoint_callback=False,
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deterministic=True,
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logger=False,
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replace_sampler_ddp=False,
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)
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trainer.fit(model)
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final_loss = trainer.train_loop.running_loss.last().item()
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errors.append(final_loss)
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time_end = time.perf_counter()
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times.append(time_end - time_start)
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return errors, times
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