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# Extracted from: https://github.com/bigscience-workshop/Megatron-DeepSpeed
import random
import unittest
import torch
from torch.nn import functional as F
from megatron.model.glu_activations import GLU_ACTIVATIONS, geglu, liglu, reglu, swiglu
class TestActivations(unittest.TestCase):
def setUp(self):
"""setup an input of reasonable size"""
seed = 11
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.batch_size = random.randint(2, 64)
self.seq_len = random.randint(256, 1025)
self.num_channels = random.randint(1, 384) * 2
self.x = torch.randn(self.batch_size, self.seq_len, self.num_channels)
self.x1, self.x2 = self.x.chunk(2, dim=-1)
# glu should halve the last dimension
self.output_shape = [self.batch_size, self.seq_len, self.num_channels // 2]
def test_shapes(self):
for activation_fn in GLU_ACTIVATIONS.values():
output = activation_fn(self.x)
self.assertEqual(list(output.shape), self.output_shape)
def test_liglu(self):
expected = self.x1 * self.x2
assert torch.allclose(liglu(self.x), expected)
def test_geglu(self):
expected = self.x1 * F.gelu(self.x2)
assert torch.allclose(geglu(self.x), expected)
def test_reglu(self):
expected = self.x1 * F.relu(self.x2)
assert torch.allclose(reglu(self.x), expected)
def test_swiglu(self):
expected = self.x1 * F.silu(self.x2)
assert torch.allclose(swiglu(self.x), expected)
if __name__ == "__main__":
ta = TestActivations()
ta.setUp()
ta.test_reglu()
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