# 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()