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47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
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import torch
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import pytest
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from llm.core.gelu import GELU
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def test_gelu_shapes_and_dtype():
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gelu = GELU()
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x = torch.randn(4, 16, 8)
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y = gelu(x)
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assert y.shape == x.shape
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assert y.dtype == x.dtype
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def test_gelu_known_values():
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gelu = GELU()
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x = torch.tensor([-3.0, 0.0, 3.0])
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y = gelu(x)
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# Сравнение с PyTorch F.gelu (которая использует точный алгоритм)
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y_ref = torch.nn.functional.gelu(x)
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diff = (y - y_ref).abs().max().item()
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assert diff < 5e-3, f"Max difference {diff} exceeds threshold"
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def test_gelu_is_smooth_and_monotonic():
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gelu = GELU()
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x = torch.linspace(-5, 5, 100)
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y = gelu(x)
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dy = y[1:] - y[:-1]
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# Проверяем, что функция GELU хотя бы локально монотонна на большинстве промежутков
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assert (dy.mean() > 0 or dy.mean() < 0)
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def test_gelu_gradients():
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gelu = GELU()
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x = torch.randn(3, 5, requires_grad=True)
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y = gelu(x)
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loss = y.sum()
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loss.backward()
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assert x.grad is not None
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assert x.grad.shape == x.shape
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def test_gelu_large_vs_small():
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gelu = GELU()
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x_pos = torch.tensor([100.0])
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x_neg = torch.tensor([-100.0])
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y_pos = gelu(x_pos)
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y_neg = gelu(x_neg)
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# Для больших положительных GELU(x) ~ x, для больших отрицательных ~0
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assert torch.allclose(y_pos, x_pos, rtol=1e-4, atol=1e-4)
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assert torch.allclose(y_neg, torch.zeros_like(x_neg), rtol=1e-4, atol=1e-4)
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