docs(core): add docstrings and unit tests for SiLU activation

- docs: expand and clarify docstrings for SiLU class and its method (mathematical formula, motivation, properties vs ReLU/GELU, usage, and references to Swish/LLM papers)
- test: add unit tests for SiLU (shape/dtype, behavior on large/small values, PyTorch reference, gradients, broadcast)
- no logic/API changes

This update improves reliability and usability of the SiLU activation module.

llm/tests/core/test_silu.py

@@ -0,0 +1,42 @@
+import torch
+import pytest
+from llm.core.silu import SiLU
+
+def test_silu_shape_and_dtype():
+    silu = SiLU()
+    x = torch.randn(3, 10, 8)
+    y = silu(x)
+    assert y.shape == x.shape
+    assert y.dtype == x.dtype
+
+def test_silu_known_values():
+    silu = SiLU()
+    x = torch.tensor([-2.0, 0.0, 2.0])
+    y = silu(x)
+    # PyTorch эталон
+    y_ref = torch.nn.functional.silu(x)
+    assert torch.allclose(y, y_ref, atol=1e-6)
+
+def test_silu_large_vs_small():
+    silu = SiLU()
+    x_pos = torch.tensor([100.0])
+    x_neg = torch.tensor([-100.0])
+    y_pos = silu(x_pos)
+    y_neg = silu(x_neg)
+    assert torch.allclose(y_pos, x_pos, rtol=1e-4, atol=1e-4)   # SiLU(x) ~ x для больших x>0
+    assert torch.allclose(y_neg, torch.zeros_like(x_neg), rtol=1e-4, atol=1e-4) # SiLU(x) ~ 0 для x<0
+
+def test_silu_gradients():
+    silu = SiLU()
+    x = torch.randn(4, 4, requires_grad=True)
+    y = silu(x)
+    loss = y.sum()
+    loss.backward()
+    assert x.grad is not None
+    assert x.grad.shape == x.shape
+
+def test_silu_broadcast():
+    silu = SiLU()
+    x = torch.randn(3, 1, 16)
+    y = silu(x)
+    assert y.shape == x.shape