def test_logsumexp(self):
mat = torch.FloatTensor([[-1, 0, 1, 10, 50], [-30, -20, 0, 20, 30],
[10, 20, 30, 40, 50], [0, 0, 0, 0, 0]])
result = lmu.logsumexp(mat, keep_mask=None, add_one=False, dim=1)
correct_result = torch.logsumexp(mat, dim=1, keepdim=True)
self.assertTrue(torch.equal(result, correct_result))
result = lmu.logsumexp(mat, keep_mask=None, add_one=True, dim=1)
correct_result = torch.logsumexp(torch.cat(
[mat, torch.zeros(mat.size(0)).unsqueeze(1)], dim=1),
dim=1,
keepdim=True)
self.assertTrue(torch.equal(result, correct_result))
keep_mask = torch.FloatTensor([[1, 1, 0, 0, 0], [0, 1, 1, 1, 0],
[0, 0, 0, 0, 0], [0, 1, 1, 0, 0]])
result = lmu.logsumexp(mat, keep_mask=keep_mask, add_one=False, dim=1)
row0 = torch.log(torch.sum(torch.exp(torch.FloatTensor(
[-1, 0])))).unsqueeze(0)
row1 = torch.log(torch.sum(torch.exp(torch.FloatTensor(
[-20, 0, 20])))).unsqueeze(0)
row2 = torch.FloatTensor([0.])
row3 = torch.log(torch.sum(torch.exp(torch.FloatTensor(
[0, 0])))).unsqueeze(0)
correct_result = torch.stack([row0, row1, row2, row3], dim=0)
self.assertTrue(torch.allclose(result, correct_result))
def test_logsumexp(self):
for dtype in [torch.float16, torch.float32, torch.float64]:
division_factor = 10 if dtype == torch.float16 else 1
mat = torch.tensor([[-1, 0, 1, 10, 50],
[-30, -20, 0, 20, 30],
[10, 20, 30, 40, 50],
[0,0,0,0,0]], dtype=dtype).to(self.device)
mat /= division_factor
result = lmu.logsumexp(mat, keep_mask=None, add_one=False, dim=1)
correct_result = torch.logsumexp(mat, dim=1, keepdim=True)
self.assertTrue(torch.equal(result, correct_result))
result = lmu.logsumexp(mat, keep_mask=None, add_one=True, dim=1)
correct_result = torch.logsumexp(torch.cat([mat, torch.zeros(mat.size(0),dtype=dtype).to(self.device).unsqueeze(1)], dim=1), dim=1, keepdim=True)
self.assertTrue(torch.equal(result, correct_result))
keep_mask = torch.tensor([[1, 1, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
[0, 1, 1, 0, 0]], dtype=dtype).to(self.device)
result = lmu.logsumexp(mat, keep_mask=keep_mask, add_one=False, dim=1)
row0_input = torch.tensor([-1, 0], dtype=dtype).to(self.device) / division_factor
row1_input = torch.tensor([-20, 0, 20], dtype=dtype).to(self.device) / division_factor
row3_input = torch.tensor([0, 0], dtype=dtype).to(self.device) / division_factor
row0 = torch.log(torch.sum(torch.exp(row0_input))).unsqueeze(0)
row1 = torch.log(torch.sum(torch.exp(row1_input))).unsqueeze(0)
row2 = torch.tensor([0.], dtype=dtype).to(self.device)
row3 = torch.log(torch.sum(torch.exp(row3_input))).unsqueeze(0)
correct_result = torch.stack([row0, row1, row2, row3], dim=0)
rtol = 1e-2 if dtype == torch.float16 else 1e-5
self.assertTrue(torch.allclose(result, correct_result, rtol=rtol))
def test_logsumexp(self):
for dtype in TEST_DTYPES:
rtol = 1e-2 if dtype == torch.float16 else 1e-5
mat = torch.tensor([[-1, 0, 1, 10, 50],
[-300, -200, -100, -50, -20],
[-300, -200, 0, 200, 300],
[100, 200, 300, 400, 500],
[0,0,0,0,0]], dtype=dtype, requires_grad=True).to(self.device)
result = lmu.logsumexp(mat, keep_mask=None, add_one=False, dim=1)
torch.mean(result).backward(retain_graph=True)
correct_result = torch.logsumexp(mat, dim=1, keepdim=True)
self.assertTrue(torch.allclose(result, correct_result, rtol=rtol))
result = lmu.logsumexp(mat, keep_mask=None, add_one=True, dim=1)
torch.mean(result).backward(retain_graph=True)
correct_result = torch.logsumexp(torch.cat([mat, torch.zeros(mat.size(0),dtype=dtype).to(self.device).unsqueeze(1)], dim=1), dim=1, keepdim=True)
self.assertTrue(torch.allclose(result, correct_result, rtol=rtol))
keep_mask = torch.tensor([[1, 1, 0, 0, 0],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
[0, 1, 1, 0, 0]], dtype=torch.bool).to(self.device)
result = lmu.logsumexp(mat, keep_mask=keep_mask, add_one=False, dim=1)
torch.mean(result).backward()
row0_input = torch.tensor([-1, 0], dtype=dtype).to(self.device)
row1_input = torch.tensor([-300, -200, -100, -50, -20], dtype=dtype).to(self.device)
row2_input = torch.tensor([-200, 0, 200], dtype=dtype).to(self.device)
row4_input = torch.tensor([0, 0], dtype=dtype).to(self.device)
row0 = torch.logsumexp(row0_input, dim=0).unsqueeze(0)
row1 = torch.logsumexp(row1_input, dim=0).unsqueeze(0)
row2 = torch.logsumexp(row2_input, dim=0).unsqueeze(0)
row3 = torch.tensor([0.], dtype=dtype).to(self.device)
row4 = torch.logsumexp(row4_input, dim=0).unsqueeze(0)
correct_result = torch.stack([row0, row1, row2, row3, row4], dim=0)
self.assertTrue(torch.allclose(result, correct_result, rtol=rtol))
