mirror of
https://github.com/pese-git/simple-llm.git
synced 2026-01-23 21:14:17 +00:00
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import torch
|
||
import pytest
|
||
from simple_llm.transformer.feed_forward import FeedForward
|
||
|
||
class TestFeedForward:
|
||
@pytest.fixture
|
||
def ff_layer(self):
|
||
return FeedForward(emb_size=512)
|
||
|
||
def test_initialization(self, ff_layer):
|
||
assert isinstance(ff_layer._layer1, torch.nn.Linear)
|
||
assert isinstance(ff_layer._layer2, torch.nn.Linear)
|
||
assert isinstance(ff_layer._relu, torch.nn.ReLU)
|
||
assert isinstance(ff_layer._dropout, torch.nn.Dropout)
|
||
|
||
assert ff_layer._layer1.in_features == 512
|
||
assert ff_layer._layer1.out_features == 2048
|
||
assert ff_layer._layer2.in_features == 2048
|
||
assert ff_layer._layer2.out_features == 512
|
||
|
||
def test_forward_pass_shape(self, ff_layer):
|
||
batch_size = 4
|
||
seq_len = 10
|
||
x = torch.randn(batch_size, seq_len, 512)
|
||
output = ff_layer(x)
|
||
|
||
assert output.shape == (batch_size, seq_len, 512)
|
||
|
||
def test_dropout_training(self):
|
||
ff_layer = FeedForward(512, dropout=0.5)
|
||
ff_layer.train()
|
||
x = torch.randn(2, 5, 512)
|
||
output = ff_layer(x)
|
||
|
||
# Проверяем, что dropout действительно работает в режиме обучения
|
||
assert not torch.allclose(output, ff_layer._layer2(ff_layer._relu(ff_layer._layer1(x))))
|
||
|
||
def test_dropout_eval(self):
|
||
ff_layer = FeedForward(512, dropout=0.5)
|
||
ff_layer.eval()
|
||
x = torch.randn(2, 5, 512)
|
||
output = ff_layer(x)
|
||
|
||
# В eval режиме dropout не должен работать
|
||
expected = ff_layer._layer2(ff_layer._relu(ff_layer._layer1(x)))
|
||
assert torch.allclose(output, expected)
|
||
|
||
def test_dtype_preservation(self, ff_layer):
|
||
x = torch.randn(2, 5, 512, dtype=torch.float64)
|
||
output = ff_layer(x)
|
||
assert output.dtype == torch.float64
|