156 lines
4.1 KiB
Python
156 lines
4.1 KiB
Python
import time
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.utils.data import Dataset, DataLoader
|
|
|
|
from pytorch_lightning import Trainer, LightningModule
|
|
|
|
|
|
class AverageDataset(Dataset):
|
|
def __init__(self, dataset_len=300, sequence_len=100):
|
|
self.dataset_len = dataset_len
|
|
self.sequence_len = sequence_len
|
|
self.input_seq = torch.randn(dataset_len, sequence_len, 10)
|
|
top, bottom = self.input_seq.chunk(2, -1)
|
|
self.output_seq = top + bottom.roll(shifts=1, dims=-1)
|
|
|
|
def __len__(self):
|
|
return self.dataset_len
|
|
|
|
def __getitem__(self, item):
|
|
return self.input_seq[item], self.output_seq[item]
|
|
|
|
|
|
class ParityRNN(LightningModule):
|
|
def __init__(self):
|
|
super(ParityRNN, self).__init__()
|
|
self.rnn = nn.LSTM(10, 20, batch_first=True)
|
|
self.linear_out = nn.Linear(in_features=20, out_features=5)
|
|
|
|
def forward(self, x):
|
|
seq, last = self.rnn(x)
|
|
return self.linear_out(seq)
|
|
|
|
def training_step(self, batch, batch_nb):
|
|
x, y = batch
|
|
y_hat = self(x)
|
|
loss = F.mse_loss(y_hat, y)
|
|
return {'loss': loss}
|
|
|
|
def configure_optimizers(self):
|
|
return torch.optim.Adam(self.parameters(), lr=0.02)
|
|
|
|
def train_dataloader(self):
|
|
return DataLoader(AverageDataset(), batch_size=30)
|
|
|
|
|
|
@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
|
|
def test_pytorch_parity(tmpdir):
|
|
"""
|
|
Verify that the same pytorch and lightning models achieve the same results
|
|
:param tmpdir:
|
|
:return:
|
|
"""
|
|
num_epochs = 2
|
|
num_rums = 3
|
|
|
|
lightning_outs, pl_times = lightning_loop(ParityRNN, num_rums, num_epochs)
|
|
manual_outs, pt_times = vanilla_loop(ParityRNN, num_rums, num_epochs)
|
|
# make sure the losses match exactly to 5 decimal places
|
|
for pl_out, pt_out in zip(lightning_outs, manual_outs):
|
|
np.testing.assert_almost_equal(pl_out, pt_out, 8)
|
|
|
|
|
|
def set_seed(seed):
|
|
np.random.seed(seed)
|
|
torch.manual_seed(seed)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.manual_seed(seed)
|
|
|
|
|
|
def vanilla_loop(MODEL, num_runs=10, num_epochs=10):
|
|
"""
|
|
Returns an array with the last loss from each epoch for each run
|
|
"""
|
|
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
|
|
errors = []
|
|
times = []
|
|
|
|
for i in range(num_runs):
|
|
time_start = time.perf_counter()
|
|
|
|
# set seed
|
|
seed = i
|
|
set_seed(seed)
|
|
|
|
# init model parts
|
|
model = MODEL()
|
|
dl = model.train_dataloader()
|
|
optimizer = model.configure_optimizers()
|
|
|
|
# model to GPU
|
|
model = model.to(device)
|
|
|
|
epoch_losses = []
|
|
for epoch in range(num_epochs):
|
|
|
|
# run through full training set
|
|
for j, batch in enumerate(dl):
|
|
x, y = batch
|
|
x = x.cuda(0)
|
|
y = y.cuda(0)
|
|
batch = (x, y)
|
|
|
|
loss_dict = model.training_step(batch, j)
|
|
loss = loss_dict['loss']
|
|
loss.backward()
|
|
optimizer.step()
|
|
optimizer.zero_grad()
|
|
|
|
# track last epoch loss
|
|
epoch_losses.append(loss.item())
|
|
|
|
time_end = time.perf_counter()
|
|
times.append(time_end - time_start)
|
|
|
|
errors.append(epoch_losses[-1])
|
|
|
|
return errors, times
|
|
|
|
|
|
def lightning_loop(MODEL, num_runs=10, num_epochs=10):
|
|
errors = []
|
|
times = []
|
|
|
|
for i in range(num_runs):
|
|
time_start = time.perf_counter()
|
|
|
|
# set seed
|
|
seed = i
|
|
set_seed(seed)
|
|
|
|
# init model parts
|
|
model = MODEL()
|
|
trainer = Trainer(
|
|
max_epochs=num_epochs,
|
|
show_progress_bar=False,
|
|
weights_summary=None,
|
|
gpus=1,
|
|
early_stop_callback=False,
|
|
checkpoint_callback=False,
|
|
distributed_backend='dp',
|
|
)
|
|
trainer.fit(model)
|
|
|
|
final_loss = trainer.running_loss.last().item()
|
|
errors.append(final_loss)
|
|
|
|
time_end = time.perf_counter()
|
|
times.append(time_end - time_start)
|
|
|
|
return errors, times
|