Полная реализация FeedForward

- Основной класс FeedForward
- Тесты для всех функций
- Пример использования с визуализацией
- Документация с блок-схемой

tests/test_feed_forward.py

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+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.net, torch.nn.Sequential)
+        assert len(ff_layer.net) == 4
+        assert isinstance(ff_layer.net[0], torch.nn.Linear)
+        assert isinstance(ff_layer.net[1], torch.nn.ReLU)
+        assert isinstance(ff_layer.net[2], torch.nn.Linear)
+        assert isinstance(ff_layer.net[3], torch.nn.Dropout)
+        
+        assert ff_layer.net[0].in_features == 512
+        assert ff_layer.net[0].out_features == 2048
+        assert ff_layer.net[2].in_features == 2048
+        assert ff_layer.net[2].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
+        layers = ff_layer.net
+        no_dropout = layers[2](layers[1](layers[0](x)))
+        assert not torch.allclose(output, no_dropout)
+
+    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
+        layers = ff_layer.net
+        expected = layers[2](layers[1](layers[0](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