File size: 1,382 Bytes
7894dce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
import pytest
import torch
import megatron.core.utils as util
import numpy as np
def test_divide_properly():
assert util.divide(4,2) == 2
def test_divide_improperly():
with pytest.raises(AssertionError):
util.divide(4,5)
def test_global_memory_buffer():
global_memory_buffer = util.GlobalMemoryBuffer()
obtained_tensor = global_memory_buffer.get_tensor((3,2), torch.float32, "test_tensor")
expected_tensor = torch.empty((3,2), dtype=torch.float32, device=torch.cuda.current_device())
assert torch.equal(obtained_tensor, expected_tensor)
def test_make_viewless_tensor():
inp = torch.rand((3,4))
assert(torch.equal(inp, util.make_viewless_tensor(inp, True, True)))
assert(torch.equal(inp, util.make_viewless_tensor(inp, True, False)))
def test_safely_set_viewless_tensor_data():
tensor = torch.zeros((3,4))
new_data_tensor = torch.tensor(np.random.rand(3,4))
util.safely_set_viewless_tensor_data(tensor, new_data_tensor)
assert(torch.equal(tensor, new_data_tensor))
def test_assert_viewless_tensor():
tensor = torch.rand((3,4))
assert(torch.equal(util.assert_viewless_tensor(tensor), tensor))
input_tensor_list=[tensor,tensor,tensor]
output_tensor_list = util.assert_viewless_tensor(input_tensor_list)
for inp,out in zip(input_tensor_list, output_tensor_list):
assert(torch.equal(inp,out))
|